a cost-based optimization of a fiberboard pressing plant...
TRANSCRIPT
By
MONARDO GIANI
Under the Supervision of
Professor Lin Ma and
Professor Prasad Yarlagadda
A Thesis Submitted for the degree of Master of Engineering
April 2009
A cost-based optimization of a fiberboard pressing plant using Monte-Carlo simulation (A reliability program)
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A cost-based optimization of a fibreboard pressing plant using Monte-Carlo simulation (A reliability program)
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Abstract
In this research the reliability and availability of fiberboard pressing plant is
assessed and a cost-based optimization of the system using the Monte- Carlo
simulation method is performed.
The woodchip and pulp or engineered wood industry in Australia and around
the world is a lucrative industry. One such industry is hardboard. The pressing
system is the main system, as it converts the wet pulp to fiberboard. The
assessment identified the pressing system has the highest downtime throughout the
plant plus it represents the bottleneck in the process.
A survey in the late nineties revealed there are over one thousand plants
around the world, with the pressing system being a common system among these
plants. No work has been done to assess or estimate the reliability of such a
pressing system; therefore this assessment can be used for assessing any plant of
this type.
Keywords: Hardboard, Fiberboard, Pressing system, Reliability, Availability,
Monte-Carlo simulation.
Acknowledgements I would like to acknowledge the following key persons for their contribution in
completing this research: Professor Lin Ma, my principle supervisor for her much appreciated constant
guidance and assistance throughout the lengthy research period. Professor Dr Professor Prasad Yarlagadda my associate supervisor for his
guidance and assistance. Mr. Mick Drew, Director of ARMS reliability engineers for providing the simulation
software. Mr. Wayne Chilton, Maintenance Manager from Australian Hardboards Limited.
Wayne provided the missing and very much needed information about the hardboard manufacturing equipment performance history.
Mrs. Julie McGillivray, work colleague from Australian Hardboards Limited, for
her extraordinary effort and time in completing this research. To my beloved wife, Abeer for her patience with me all the time taken to
complete this work.
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TABLE OF CONTENTS
CHAPTER ONE – INTRODUCTION………………………………………………..11 1.1 Overview of the woodchip and pulp industry……………………….11
1.2 Products produced at the plant………………………………………12
1.3 Definition of the research problem and its importance...................14
1.4 Research objectives…………………………………………………..16
1.5 Expected outcome of the research………………………………….17
CHAPTER TWO - LITERATURE REVIEW………………………………………...18 2.1 Introduction ................................................................................... 18
2.2 Maintenance management ............................................................ 20
2.3 The scope of maintenance ............................................................ 24
2.3.1 Frameworks .................................................................................. 26
2.3.1.1 Reliability-Centered Maintenance (RCM) ...................................... 27
2.3.1.2 Total Productive Maintenance ....................................................... 28
2.3.1.3 Business-Centered Maintenance (BCM)…………………………….29
2.3.1.4 Maintenance Excellence ............................................................... 30
2.3.1.5 Other frameworks .......................................................................... 32
2.3.2 Reliability Assessment and analysis ............................................. 33
2.3.2.1 Analytical models .......................................................................... 34
2.3.2.1.1 Basic principles of probability based ............................................. 35
2.3.2.1.2 Markovian theory ........................................................................... 37
2.3.2.1.3 Bayesian theory ............................................................................ 38
2.3.2.1.4 Poisson process ............................................................................ 41
2.3.2.1.5 Models based on the Condition monitoring data ........................... 42
2.3.2.2 Other techniques ........................................................................... 43
2.3.2.2.1 Condition monitoring and fault diagnosis (CMFD) ......................... 43
2.3.2.2.2 Fault tree and root cause analysis ................................................ 44
2.3.2.2.3 Reliability block diagram (RBD) ..................................................... 45
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2.3.2.2.4 Failure mode, effects and criticality analysis (FMECA) ................. 46
2.3.2.2.5 Monte Carlo simulation .................................................................. 47
2.3.3 Maintenance optimization .............................................................. 48
2.3.3.1 Cost based optimization policies ................................................... 48
2.3.3.2 Risk based optimization policies ................................................... 49
2.3.3.3 Other Maintenance strategies ....................................................... 50
2.3.3.4 Discussion ..................................................................................... 51
CHAPTER THREE - FIBREBOARD PRODUCTION PROCESS ....................... 53 3.1 Introduction ................................................................................... 53
3.2 The Hardboard production process ............................................... 53
3.2.1 Wood chipping and mixing ............................................................ 54
3.2.2 Pulp preparation and storage stage .............................................. 54
3.2.3 Pulp forming stage ........................................................................ 55
3.2.4 Sizing stage ................................................................................... 56
3.2.5 Wet mat transfer stage (plate circuit) ............................................ 57
3.2.6 The pressing stage ........................................................................ 57
3.2.7 Tempering stage ........................................................................... 58
3.2.8 Humidification stage ...................................................................... 59
3.2.9 Grading and secondary process ................................................... 59
3.3 System selection ........................................................................... 60
3.4 An overview of the selected system (Pressing plant) .................... 62
3.4.1 Press Technical Data: ................................................................... 62
3.4.2 Press technical description ............................................................ 62
3.4.3 System functionality ...................................................................... 64
3.5 System boundaries definition ........................................................ 67
CHAPTER FOUR - SYSTEM RELIABILITY ASSESSMENT…………………….70
4.1 Introduction ................................................................................... 70
4.1.1 Functional Failure Mode, Effect and Criticality Analysis (FMECA) 70
4.1.2 Modeling the failure data ............................................................... 71
4.1.2.1 Press Rams ................................................................................... 73
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4.1.2.1.1 Estimating the MTBF ..................................................................... 73
4.1.2.1.2 Estimating the MTTR..................................................................... 75
4.1.2.2 Solenoid valves S1, S2, S3, S4 and S10 ...................................... 76
4.1.2.2.1 Estimating the MTTF ..................................................................... 76
4.1.2.3 Low-pressure pumps ..................................................................... 76
4.1.2.3.1 Estimating the MTBF ..................................................................... 76
4.1.2.3.2 Estimating the MTTR..................................................................... 79
4.1.2.4 High-pressure pumps 1-4 .............................................................. 80
4.1.2.4.1 Estimating the failure model .......................................................... 80
4.1.2.4.2 Estimating the MTTR..................................................................... 83
4.1.2.5 Boiler set ....................................................................................... 84
4.1.2.5.1 Estimating the MTBF ..................................................................... 84
4.1.2.5.2 Estimating the MTTR..................................................................... 88
4.1.2.6 Pre-fill and exhaust valve no.6………………………………………..89
4.1.2.6.1 Estimating failure model MTBF……………………………………….89
4.1.2.6.2 Estimating repair model MTTR ...................................................... 90
4.1.2.7 Blocking magnet ............................................................................ 91
4.1.2.7.1 Estimating failure model parameters ............................................. 91
4.1.2.8 High-pressure pump no. 5 ............................................................. 93
4.1.2.9 Non-return valves .......................................................................... 94
4.1.2.10 Thruster valves 1- 4 ...................................................................... 94
4.1.2.10.1 Estimating the MTBF ..................................................................... 94
4.1.2.10.2 Estimating the MTTR..................................................................... 98
4.1.2.11 Tubular guides .............................................................................. 99
4.1.2.11.1 Estimating MTBF ........................................................................... 99
4.1.2.11.2 Estimating MTTR ........................................................................ 102
4.1.2.12 Heating platens leaks .................................................................. 104
4.1.2.12.1 Estimating the MTBF ................................................................... 104
4.1.2.12.2 Estimating the MTTR................................................................... 105
4.1.2.13 Link pipes .................................................................................... 106
4.1.2.13.1 Estimating the MTBF ................................................................... 106
4.1.2.13.2 Estimating the MTTR................................................................... 107
4.1.2.14 Bleeding valve no.7 ..................................................................... 108
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4.1.2.14.1 Estimating the MTTF ................................................................... 108
4.1.2.15 Temperature sensor .................................................................... 109
4.1.2.16 Exhaust fan bearings .................................................................. 109
4.1.2.17 Pressure sensor .......................................................................... 110
4.1.2.18 Timer ........................................................................................... 111
4.1.2.19 Hydrauphore tank ........................................................................ 111
4.1.2.19.1 Estimating the MTBF ................................................................... 111
4.1.2.19.2 Estimating the MTTR................................................................... 115
4.1.2.20 Valve no.11 ................................................................................. 115
4.1.2.20.1 Estimating the MTBF ................................................................... 115
4.1.2.20.2 Estimating the MTTR................................................................... 116
4.1.2.21 Air compressor ............................................................................ 116
4.1.2.21.1 Estimating the MTBF ................................................................... 116
4.1.2.21.2 Estimating the MTTR................................................................... 118
4.1.2.22 Control valves no.8 & 9 ............................................................... 118
4.1.2.23 Change over valve no.5 .............................................................. 121
4.1.2.24 PLC ............................................................................................. 121
4.1.2.25 Piping system .............................................................................. 122
4.1.2.26 Platens off position ...................................................................... 122
4.1.2.26.1 Estimating the MTBF ................................................................... 122
4.1.2.26.2 Estimating the MTTR................................................................... 123
4.1.2.27 Hydrauphore relief valve ............................................................. 124
4.1.2.28 Hydraulic storage tank parameters ............................................. 125
4.1.3 Fault Tree Analysis (FTA) ........................................................... 125
4.1.3.1 Constructing the fault tree model ................................................ 127
4.1.3.2 Estimating the system probability of failure from FTA ................. 131
4.1.4 Reliability Block Diagram Analysis .............................................. 135
4.1.4.1 Estimating the system reliability .................................................. 136
4.1.5 Estimating the system Availability ............................................... 140
4.2 Monte Carlo Simulation ............................................................... 143
4.3 Evaluating the system performance ............................................ 146
4.4 Optimizing the system ................................................................. 154
4.4.1 Mathematical models and equations ........................................... 155
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4.4.1.1 Modeling the optimal replacement times for equipment which
it’s operating cost increases with usage ...................................... 155
4.4.1.2 Modeling optimal preventive replacement interval of an item
subject to breakdown .................................................................. 157
4.4.1.3 Modeling of the optimal spare parts preventive replacement
age and constant failure interval ................................................. 159
4.4.1.4 Modeling the optimal inspection frequency ................................. 160
4.4.1.5 Availability ................................................................................... 162
4.5 Obtaining the optimized system results ....................................... 162
CHAPTER FIVE - SENSITIVITY ANALYSIS .................................................... 167
5.1 Introduction ................................................................................. 167
5.2 Comparing system data with and without dependencies before
optimization ................................................................................. 167
5.2.1 Estimating the simulated system reliability
5.2.2 Estimating the simulated system Availability.................................170
5.3 Comparing the number of failures and downtime..........................174
5.4 Criticality values estimation...........................................................175
5.5 Comparing the different cost associated with the system before
and after optimization....................................................................176
5.6 Conclusions...................................................................................177
CHAPTER SIX – CONCLUSIONS.....................................................................178
6.1 Introduction....................................................................................178
6.2 Major findings................................................................................179
6.3 Limitations and Uncertainties.........................................................182
6.4 Future studies................................................................................184
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REFERENCES .................................................................................................. 186 APPENDIX 1 ..................................................................................................... 198 APPENDIX 2 ..................................................................................................... 199 APPENDIX 3 ..................................................................................................... 200 APPENDIX 4 ..................................................................................................... 201 APPENDIX 5 ..................................................................................................... 203 APPENDIX 6 ..................................................................................................... 211 APPENDIX 7 ..................................................................................................... 212 APPENDIX 8 ..................................................................................................... 214 APPENDIX 9 ..................................................................................................... 222 APPENDIX 10 ................................................................................................... 225 APPENDIX 11………………………………………………………………………...227 APPENDIX 12…………………………………………………………………….…..229
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INDEX OF FIGURES
Figure 1: Changing expectations of maintenance (Moubray 1997) .......... 18
Figure 2: Asset management versus maintenance management (van
Voorthuysen 2005) ..................................................................... 19
Figure 3: Growing expectations of maintenance (Moubray 1997) ............ 22
Figure 4: Routine and non routine maintenance activities (Hasting 2001) 25
Figure 5: Maintenance science classification (Sun 2006) ......................... 26
Figure 6: The RCM process (Jardine 2006) ............................................. 28
Figure 7: Modified structural approach to achieving maintenance
excellence by Campbell 1995. .................................................. 30
Figure 8: A schematic diagram of the production flow process ................ 59
Figure 9: The research process flow chart ............................................... 60
Figure 10: breakdown of the plant areas downtime .................................... 61
Figure 11: Schematic drawing of the press ................................................ 64
Figure 12: Block diagram of the pressing process ...................................... 65
Figure 13: illustrates the press cycle working pressures ............................ 67
Figure 14: illustrates the pressing plant and its components
(Sunds defibrators-Sweden) ....................................................... 68
Figure 15: A schematic diagram of the system boundaries ........................ 68
Figure 16: The press rams weibull plot ....................................................... 74
Figure 17: Testing the LPPs data against the HPP .................................... 77
Figure 18: Low pressure pumps log TTF-log N(T) ...................................... 78
Figure 19: Low pressure pumps repair model graph .................................. 80
Figure 20: Testing the HPPs 1-4 data against the HPP ............................. 81
Figure 21: HPPs 1-4 log failure data .......................................................... 81
Figure 22: HPPs 1-4 repair model .............................................................. 84
Figure 23: Boiler TBF/N(T) graph ............................................................... 85
Figure 24: Boiler log t-log N(T) ................................................................... 86
Figure 25: Boiler repair model graph .......................................................... 89
Figure 26: Valve no.6 Weibull distribution graph ........................................ 90
Figure 27: V/v no.6 exponential distribution repair graph ........................... 91
Figure 28: Blocking magnet failure model graph ........................................ 92
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Figure 29: Thruster valves TBF against N(T) ............................................. 95
Figure 30: Thruster valves log TBF-log N(T) .............................................. 96
Figure 31: Thruster valves log-normal repair model plot ............................ 98
Figure 32: Tubular guides TBF-N(T) ........................................................ 100
Figure 33: Tubular guides log TBF-log N(T) ............................................. 101
Figure 34: Log-normal plot of tubular guides repair data .......................... 103
Figure 35: Heating platen exponential repair model ................................. 105
Figure 36: Link pipes repair model ........................................................... 108
Figure 37: Log-normal distribution of centrefugal pump and circuit breaker
failure rates (independent samples)(T.R.Moss) ....................... 108
Figure 38: Pressing system RBD ............................................................. 135
Figure 39: High pressure pumps 1-4 assembly ........................................ 136
Figure 40: Low pressure pumps ............................................................... 140
Figure 41: Low pressure assembly ........................................................... 140
Figure 42: Hydrauphore sub assembly ..................................................... 140
Figure 43: Heating system ....................................................................... 140
Figure 44: Remaining sub assembly ........................................................ 140
Figure 45: Press assembly ....................................................................... 140
Figure 46: Pressing plant simplified .......................................................... 140
Figure 47: Illustrates the used simulation sequence ................................. 145
Figure 48: Sub-systems unavailability profiles before optimization .......... 153
Figure 49: System cost profile before optimization ................................... 154
Figure 50: Short term deterministic optimization ...................................... 156
Figure 51: Optimal preventive replacement .............................................. 158
Figure 52: Optimal inspection frequency to maximize profit ..................... 161
Figure 53: Optimal inspection frequency to minimize downtime ............... 162
Figure 54: Unavailability profile of plant sub-system after optimization .... 165
Figure 55: System cost profile after optimization ...................................... 166
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INDEX OF TABLES
Table 1: Press rams failure data ................................................................ 73
Table 2: The press rams TTF .................................................................... 74
Table 3: Press rams repair times ............................................................... 75
Table 4: Low pressure pumps TTF ............................................................ 77
Table 5: Ranked ttf,N(T),log cumulative and log N(T) ............................... 77
Table 6: Low pressure pumps repair data ................................................. 79
Table 7: HPPs failure data re-arranged ..................................................... 81
Table 8: HPPs1-4 times to repaire ............................................................ 83
Table 9: Boiler TBF/N(T) ........................................................................... 85
Table 10: Boiler log N(T), log TBF ............................................................... 86
Table 11: Boiler repair data ......................................................................... 88
Table 12: TTF and TTR for valve no.6 ........................................................ 89
Table 13: Blocking magnet TTF .................................................................. 91
Table 14: Thruster valves TBFs .................................................................. 94
Table 15: Thruster valves TBFs against N(T) .............................................. 95
Table 16: Thruster valves log TBF against log N(T) .................................... 96
Table 17: Thruster valves repair data .......................................................... 98
Table 18: Tubular guides’ failure data ......................................................... 99
Table 19: Tubular guides TBF-N(T) ............................................................. 99
Table 20: Tubular guides Log TBF-Log N(T) ............................................. 100
Table 21: Tubular guides repair data ......................................................... 103
Table 22: Heating platens failure data ....................................................... 104
Table 23: Heating platens repair data ....................................................... 105
Table 24: Link pipes failure data ................................................................ 106
Table 25: Link pipes ranked repair times ................................................... 107
Table 26: Generic data from different sources .......................................... 113
Table 27: Ordered failure rates data estimates ......................................... 113
Table 28: Hydrophore tank design attributes ............................................. 114
Table 29: Operating attributes ................................................................... 114
Table 30: Environment attributes ............................................................... 114
Table 31: Design attributes ....................................................................... 117
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Table 32: Generic data from different sources .......................................... 119
Table 33: Ordered failure rates estimates ................................................. 119
Table 34: Design attributes ....................................................................... 120
Table 35: Operating attributes ................................................................... 120
Table 36: Environment attributes ............................................................... 120
Table 37: Platens’ failure data ................................................................... 123
Table 38: Platens’ off-position repair data ................................................. 124
Table 39: Illustrates obtaining TTF values by simulation ........................... 147
Table 40: System profile before optimizing ............................................... 152
Table 41: System effects data before optimization .................................... 153
Table 42: C(tr) values for different values of tr .......................................... 163
Table 43: C(tp) values for different values of tp ......................................... 164
Table 44: Press system effects data after optimization…………………...136
Table 45: System profile after optimization……………….………………...164
Table 46: Comparison of system reliability & availability with and without
dependencies before and after optimization………………………173
Table 47: The number of expected failures and downtime of the system
Before and after optimization……………………………………….175
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CHAPTER ONE - INTRODUCTION
1.1 Overview of the woodchip and pulp industry The woodchip and pulp or engineered wood industry in Australia
and around the world is a lucrative industry; the industry's turnover in
Australia was $9.91 billion, or around one per cent of GDP in 1992-93
(latest available data). The industry employs approximately 82,500
people, according to the latest labor force estimates from the Australian
Bureau of Statistics. It’s a mature industry with a strong market. Here in
Australia, the Australian timber industry is going through unprecedented
change. There are significant opportunities for growth in the production
and sales of high value timber products in all Australian species groups
[1].
One such product is hardboard, which is made of hardwood, i.e.
Fiberboard with a density exceeding 0.80g/cm3.
Hardboard is a high density 100% all-natural fiber board, also
known as Masonite™. Made from superior fibers, Masonite™ is
capable of bringing enhanced stability, water resistance and durability
into the designed systems of the modern building, joinery, furniture
markets. Masonite™ was developed in Laurel, Mississippi in 1920 by
William H Mason, an expert on wood derivatives and an associate of
Thomas Edison. Masonite™ has a high strength to thickness ratio,
producing a lightweight, versatile, multi-purpose building, fabrication
and packaging material. Hardboard production uses only naturally
occurring wood glues (lignin) to bond fibers, eliminating the need for
synthetic glues and resins. Masonite™ panels are dimensionally stable
in service and have higher moisture resistance properties than many
other wood based panel products [2].
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1.2 Products produced at the plant The plant produces different types of products. Listed below are
some of these products.
• Masonite™ Pegboard Masonite™ Pegboard has unlimited applications. It
is an ideal storage solution used Australia-wide from the
garage to the showroom.
• Masonite™ Readifix Not only is Readifix pre-primed, it's preconditioned
and has beveled edges so all that is needed is to butt
panels together and apply the top coat.
• Masonite™ Underlay Masonite™ Underlay is used to cover timber,
particleboard or concrete flooring. Masonite™ Underlay
is the only product that complies to Australian Standards
and is recommended by leading vinyl manufacturers.
• Masonite™ Standard Masonite™ Standard will withstand intermittent
wetting without loss of strength.
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• Masonite™ Tempered Masonite™ Tempered is tough, strong, indent and
moisture resistant.
• Masonite™ White-Cote Masonite™ White-Cote is a pre-finished moisture-
resistant board ideal for cabinetry, furniture backs and
bottoms.
• Masonite™ Chalkboard Masonite™ Chalkboard is used from commercial
signage to playroom blackboards.
• M4 & M6 Braceboard M4 Braceboard provides structural strength with
easy to follow nail markings and instructions on each
sheet and is competitively priced. Braceboard is used to
brace timber frames in brick veneer construction. M6 is
ideal for narrow paneling requirements i.e. next to
windows.
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• Deco Hardboard Specially designed panels which offer "design
flexibility" by blending popular holing configurations with
four major finish categories including timber veneer, vinyl,
laminates and Color-Cote (painted).
1.3 Definition of the research problem and its importance:
The predicted volume of hardwood pulpwood produced in
Australian plantations will increase from around 0.7 million cubic
meters per annum in the 1995-99 period to over 10 million cubic
meters per annum in the period 2035-39 [3]. The increase in the
population, the rising demand for the wood products to be used in the
housing industry [4], and since hardboard has established itself as a
reliable product for use in the dwelling construction, furniture and
cabinetry industry for its unique characteristics, it is forecast that it still
can retain its niche market if it can introduce new technologies and
reduce its maintenance cost.
While most wood products enjoy an increasing market, the
hardboard market is foreseen as losing ground to other products, the
reasons for this as cited by Gunnersen [5] are:
Competition from similar products.
The world wide situation of over-supply of medium density
fiberboard is maintaining downward pressure on prices in
those markets which both MDF and hardboard supply.
Selling prices not increasing, with costs rising by 4% p.a.
There are constraints with existing plant and infrastructure
on cost reductions in the manufacture of hardboard which
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put a floor under this industry's capability to compete on
price against products employing newer technology,
especially those using the dry process.
High levels of maintenance cost due to the age of the plants
still operating in Australia and the fact that production
involves a wet process.
For the above reasons, and the importance of this industry, this
research was undertaken. Searching the literature yielded no results of
any study has been done to improve the manufacturing process for
this type of industry.
A survey of the business in concern revealed that although the
organization is still capturing a good share of the market, but it’s
loosing this share in alarming levels due to the increasing unavailability
of the machines in its plant, and the increasing of its maintenance cost
to about 46% of its operating budget.
Fig 10 in section 3 illustrates the downtime of the different
process areas, revealed by the plant survey, with one major area, the
pressing system which is the highest contributor to the plant downtime.
The actual numbers were removed due to its sensitivity.
The press plant is used for transforming the wet pulp into dry
board, by draining the moisture out of the pulp by means of
compression and heat. The production of wood fiber by mixing wood
chips with water is known as the wet method.
The pressing plant has the highest downtime; therefore this area
will be targeted in this research. This is in line with the fact that the
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pressing plant is the most important part of the process, as in this part
the transformation of the pulp into board occurs, plus it represents the
bottleneck of the whole process, which led the company to increase
the capacity of the press to thirty openings. The new design has
increased the capacity, but introduced additional problems to the
system
1.4 Research objectives The objectives of the thesis are as follow:
1. Learn the principles of Reliability Centre Maintenance (RCM),
as part of asset management and apply it to real life project.
2. Optimize the performance of a complex repairable system
under imperfect repairs by reducing the cost of the associated
maintenance activities, and increasing the availability of the
system.
3. From experience as being Maintenance personnel on the shop
floor, people involved in the maintenance part of the business
are hands on. i.e. they are the ones who implements the
maintenance strategies, and with the continuing pressure to
have the equipment performing satisfactory, they do not have
the time to carry out detailed and thorough research to come up
with new models and methodologies to implement in order to
improve their systems, plus they need an easy to understand
applications in order to make the job less complicated, plus to
convince the tradesmen of the viability of implementing such
programs and how it’s going to reflects positively on the way
that they do the task.
4. To lay the foundation for further studies in this field for this type
of industry.
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1.5 Expected outcome of research
The expected outcome hoped to be achieved through this
research is:
• Optimizing the performance of the highest downtime section of a
hardboard plant by reducing the cost of its maintenance through
a reliability centered maintenance program by using Monte Carlo
method based simulation. Once this is achieved, it can be carried
out in the same method onto the other areas of the plant.
• An applicable reliability program that can be used by the
maintenance personnel in the engineered wood industry, able to
reduce the rising maintenance cost of the plant and bring it in-line
with the organization’s objectives to stay in business.
This program can also be applied to similar industry plants
around the world, as they all share the same manufacturing
concept.
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CHAPTER TWO - LITERATURE REVIEW 2. 2.1 Introduction
Asset management is a broad concept that is aimed at managing
return on investment [6].
Companies and organizations invest substantial amounts on
obtaining assets, and also on maintaining them, therefore they need
an effective maintenance plans to maintain these assets, prolong their
reliable life cycle as much as possible to achieve the intended
business objectives, such as provide safe working environment, better
quality product or service, have the maximum return on their
investment. And so on.
Tsang and Jardine [7]. Stated that the performance demand of
physical asset management has become more challenging as a result
of three developments:
Emerging trends of operation strategies;
Toughening societal expectations; and
Technological changes.
Figure 1: Changing expectations of maintenance (Moubray 1997)
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Figure 1 illustrates the changes expectations of maintenance. In
order to meet these demands, organizations need to focus on
improving the performance of their physical assets [8], maintenance
needed to respond to these demands and expectations changes, such
as the growing awareness of the extent to which equipment failure
effects safety and the environment, a growing awareness of the
connection between maintenance and product quality, and increasing
pressure to achieve high plant availability and to contain costs [9]. This
is achievable through processes re-engineering, optimizing
maintenance strategies, having the right human resources and
systems in place and proper planning and scheduling.
The objectives of asset management according to Van
Voorthuysen [10] are:
Minimize investment;
Minimize ownership costs;
Maximize commercial returns; and
Manage risks.
Figure 2: Asset management versus maintenance management (van
Voorthuysen 2005)
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2.2 Maintenance management Maintenance management is a sub section of the asset
management big picture, and in this thesis we will be concentrating on
this section of the asset management.
Maintenance is defined in the American Federal Standard 1037C
and from Military Standard 188 as: The care and servicing by
personnel for the purpose of maintaining equipment and facilities in
satisfactory operating condition by providing for systematic inspection,
detection, and correction of incipient failures either before they occur
or before they develop into major defects. Including tests,
measurements, adjustments, and parts replacement, performed
specifically to prevent faults from occurring [11], and Geraerd [12],
defined it as “Maintenance is all activities aimed at keeping an item in,
or restoring it to, the physical state considered necessary for the
fulfillment of its production function”, while the British Standards
Institution defines maintenance as “the combination of all technical and
administrative action intended to retain an item in, or restore it to, a
state in which it can perform its required function” [13].
Maintenance management covers every stage of the life cycle of
systems that include plant, machinery, equipment and the facilities that
hosts them, the Specification, acquisition, planning, operation,
performance evaluation, improvement, and disposal. [14]. Therefore
when the maintenance function is perceived in this wider context it is
also called Physical Asset Management (PAM).
In this thesis, we will be concentrating on the maintenance
management component of asset management.
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Management has always viewed maintenance as a supporting
and non-productive function. This is due to the fact that when a
breakdown happens it is very easy to identify that it happened due to a
lack of maintenance, or wrong maintenance, on the other hand, if a
plant is performing well and generating profits, it is very difficult to
relate that to efficient maintenance. It is easy to calculate yearly
maintenance expenditure, but it is not easy to estimate the benefit of
maintenance on the return on investment or even how it can be
measured [15].
Moubray [9] classified the evolution of maintenance or
expectations of maintenance into three generations:
a. The first generation, which covers the period up to World
War I, where industry was not very highly mechanized, and
so downtime did not matter much, and prevention of
equipment failure was not a very high priority.
b. The second generation, where the pressure of World War II
increased the demand for goods of all kinds opposing to the
high drop in industrial manpower. Machines became more
complex and industry became more dependant upon them,
and as a consequence, downtime came into sharper focus.
This led to the idea that equipment failure could, and
should, be prevented and the concept of preventive
maintenance that mainly consisted of overhauls should be
carried out at fixed intervals.
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c. Third generation, the mid seventies, where changes could
be classified under the headings of new expectations, new
research and new techniques.
Figure 3: Growing expectations of maintenance (Moubray 1997)
Maintenance is generally classified into four categories [8, 9, 16].
• Corrective maintenance (CM) - Actions carried out to
restore a defective item to a specified condition, or all
maintenance performed to correct a break down or failure
[17], this strategy started with the start of the industrial age
till before the Second World War.
• Preventive maintenance (PM) - All maintenance performed
in order to prevent a failure, or to detect a failure early, this
strategy started in the Second World War period [18].
• Predictive maintenance (PdM) - A maintenance process
based on machinery inspection, monitoring, and prediction.
Machine stops for maintenance are planned depending on
the predictions (condition-based). The terms ‘Condition
Based Maintenance’, ‘On-Condition Maintenance’, and
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‘Predictive Maintenance’ are often used interchangeably
[19]. This strategy started in the mid seventies.
• Proactive maintenance is the application of analytical
methods, tools, and techniques to eliminate failures, extend
component life, mitigate consequences, minimize
downtimes, and optimize all resources [19, 20].
There are many maintenance policies stemming from the above
categories, some of them are:
• age replacement policy;
• random age replacement policy;
• block replacement policy;
• periodic preventive maintenance policy;
• failure limit policy;
• sequential preventive maintenance policy;
• repair cost limit policy;
• repair time limit policy;
• repair number counting policy;
• reference time policy;
• mixed age policy;
• preparedness maintenance policy;
• group maintenance policy; and
• opportunistic maintenance policy [21].
Maintenance activities exist almost everywhere there is an asset,
in transportation [22-24], in power plants [25, 26], in offshore platforms
[27], software [28], manufacturing, services and many more. These
wide ranges of businesses rely on maintenance to achieve their
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objectives. Pressure has been placed on researchers to develop new
methodologies and techniques to accommodate the ever growing
demands and changes in business objectives, product quality, safety
and environmental requirements.
Some methodologies and techniques have been developed for
an individual industry [27, 29, 30], some have been developed for
specific industries, however the methodologies and techniques may be
modified to apply in other applications [31], while some have been
developed for use in other industries without modifications [32-34] [33].
2.3 The scope of maintenance The scope of maintenance has been defined differently by
researchers [35, 36], these differences in definitions are based on
whether the scope is for a component, equipment, system or the whole
process [15]. According to Hasting, [37] traditional maintenance has
been divided into routine and non routine maintenance. Non routine
tasks are carried out at convenience or when there is an opportunity,
or in the case of an emergency, which are mostly breakdowns.
Sometimes non routine maintenance is required even though there is
no breakdown. Figure 4 demonstrates routine and non routine types of
maintenance activities:
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Figure 4: Routine and non routine maintenance activities (Hasting 2001)
To gain a better understanding of the different types of
maintenance methodologies that exist, we will use the classification by
Yong Sun [38].
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Figure 5: Maintenance science classification (Sun 2006)
2.3.1 Frameworks Framework is a set of concepts linked to a planned or existing
system of methods, behaviors, functions, relationships, and objects,
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referred to as conceptual framework. Its use in research is to outline
possible courses of action, or to present a preferred approach to a
system analysis project [39, 40]. Maintenance framework provides the
structure within which to manage the maintenance and to optimize the
life cycle of the assets in line with the business objectives of an
organization.
2.3.1.1 Reliability-Centered Maintenance (RCM) Reliability-centered Maintenance was developed over the period
of thirty years [41]. One of principal milestones in its development was
a report commissioned by the United States’ Department of Defense
from United Airlines and prepared by Stanley Nowlan and Howard
Heap in 1978. RCM was developed exclusively for the aviation
industry, in the 1980s companies started to use RCM in other
industries than aviation [42], which led to RCM II. [9].
Reliability has many definitions, Doty [43] quoted it as “the
probability of a device performing its purpose adequately for the period
of time intended under the stated operating conditions”. While
Ramakumar [44] defined system reliability as “the probability that the
system will perform its intended function for a specified interval of time
under stated condition”. To relate more to the maintenance function,
Moubray [9] definition of RCM is “a process used to determine what
must be done to ensure that any physical asset continues to do
whatever its users want it to do in its present operating context”.
The aim of RCM is to preserve the system function rather than
keeping it in service, its also a systematic approach, its methodology
develops the appropriate maintenance tactics through a thorough and
rigorous decision process [8]. Figure 6 shows the RCM process.
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Figure 6: The RCM process (Jardine 2006)
2.3.1.2 Total Productive Maintenance A concept first introduced in Japan by Nakajima [45], after he
studied the American Preventive Maintenance, aims at building
healthier companies by strengthening people as well as equipment
[46], it is a people-centered methodology, whereby an operator’s role
is not limited to operating the equipment, but to be part of its
maintenance régime by giving them the sense of ownership. Under the
TPM concept, the operators do the inspection, effective lubrication,
reconditioning of deteriorated parts, cleaning and minor repairs, while
major overhauls and repairs are done by the maintenance crew. This
concept of involving the operator in enhancing the equipment
performance is called autonomous maintenance (AM) [47, 48].
The goals of TPM according to Wireman [49] are:
a. Improving equipment effectiveness.
b. Improving maintenance efficiency and effectiveness.
c. Early equipment management and maintenance prevention.
d. Training to improve the skills of all people involved.
e. Involving operators (occupants) in routine maintenance.
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2.3.1.3 Business-Centered Maintenance (BCM) BCM is a conceptual maintenance process to improve equipment
effectiveness, product quality, employees’ safety and operation
performance. Kelly [50] described it as “The structure of a
methodology for developing maintenance strategy”. It is also
described as a framework of guidelines for deciding maintenance
objectives, formulating equipment life plans and plant maintenance
schedules (Maintenance planning), designing the maintenance
organization (Maintenance doing) and setting up appropriate systems
for documentation and control (maintenance control). While Hughes
[51], described it as an attitude, concept and process of continuous
improvement in maintenance and maintenance processes, equipment
condition and performance that strives to improve overall equipment
effectiveness, operations efficiency, output quality and employee
safety.
As more and more companies try to compete and survive in the
global market, the adaptation of operational concepts such as “lean
manufacturing” and “world class manufacturing practices” are
becoming commonplace. The objective of BCM therefore, is to
maximize equipment effectiveness (improve overall total efficiency) at
the minimum total cost. The elements of BCM can be summarized as:
• Asset care strategy;
• Preventive maintenance;
• Analysis and improvement;
• Planning and scheduling;
• Information management;
• Early equipment management;
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• Training and development; and
• Maintenance facilities and tools.
2.3.1.4 Maintenance Excellence Maintenance excellence is concerned with balancing
performance, risks, and the resource inputs to achieve an optimal
solution[8], but in an industrial environment it is not so clear-cut, as it is
categorized by many uncertainties. Figure 7 shows a structural
approach to achieving maintenance excellence by Campbell [52].
Figure 7: Modified structural approach to achieving maintenance excellence by
Campbell 1995.
There are three types of goals on the route to maintenance
excellence [53]:
a. Strategic - A map that comprises the current asset
management performance level and a vision for the
performance level to be achieved must be drawn, and a
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course must be set for the destination i.e. the asset
management strategy embraced by the organization informs
the course of action.
b. Tactical - The planning and scheduling and material
management system to control the maintenance process.
c. Continuous improvement - Also called incremental or
staircase improvement, continuous improvement is a
process or productivity improvement tool intended to have a
stable and consistent growth and improvement of all the
segments of a process or processes. Continual
improvement ensures the process stabilization and further
improvement. When an organization's growth and
development is intended, identification of all the processes
and development of a measurement analysis of each
process step is necessary [40]. To enhance the up-time of
the assets, there are two complimentary methodologies [54,
55], Total Productive Maintenance (TPM), which is a people
centered methodology, and an asset centered methodology,
which is Reliability-Centered Maintenance (RCM).
Another definition for continuous improvement is the KAIZEN
concept. The Japanese term means continuous improvement and is
taken from the words 'Kai' meaning ‘continuous’ and 'Zen' meaning
‘improvement’ [40, 56].
Maintenance Excellence may also be achieved by the mixing of
more than one strategy. Yong [38] noted that these conventional
framework are not effective due to lack of proper integration. Jonsson
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[57] showed in a study on Swedish companies that preventive and
integrated maintenance were more important for companies seeking
competitive process control and flexibility. Wang, et al [58] proved that
an optimal maintenance strategy mix is necessary for increasing
availability and reliability levels of production facilities without a great
increase of investment, while Moudani [22] showed a mixed Dynamic
Programming approach (to cope with the fleet assignment problem)
and a heuristic technique (to solve the embedded maintenance
schedule problem). When applied to a medium charter airline, the
approach shows acceptability characteristics for operational staff, while
providing efficient solutions.
2.3.1.5 Other frameworks In recent years there has been some effort to develop or enhance
these frameworks, but they have limited applications and they are not
as widely used as the common known ones.
• The concept of e-Diagnostics and e-Maintenance is
proposed in the semiconductor industry. By using Internet
and information technologies, e-Diagnostics and e-
Maintenance [59] intend to provide equipment specialists
with the remote capabilities of connectivity, manipulation,
configuration, performance monitoring, and data collection
and analysis on equipment to achieve the goal of promptly
diagnosing, repairing, and maintaining equipment.
• The CIBOCOF framework used to develop a customized
maintenance concept in a specific company. Specific and
new to this framework is that the optimization problem of
maintenance is also taken into account. As such, models
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described in literature finally find a way to practice, and the
gap between theory and practice lessens [60].
• The IRCMA framework, which is based on the fact that the
historical records of RCM analysis on similar items can be
referenced and used for the current RCM analysis of a new
item. Because many common or similar items may exist in
the analyzed equipment, the repeated tasks of RCM
analysis can be considerably simplified or avoided by
revising the similar cases in conducting RCM analysis [61].
• Availability Centered Maintenance (ACM) [62].
• Generic framework for integrating the maintenance
management of built-assets [63].
2.3.2 Reliability Assessment and analysis
Reliability assessment is a conceptual and quantifiable method of
highlighting whether a plant will or will not meet its intended functions
in a safe and reliable manner. The level of the reliability and safety will
depend on the size and complexity of the plant [64], when these can
be quantified from the plant specifications, the reliability assessment
becomes necessary to determine that the plant will meet its intended
functions [65].
The need for reliability assessment arises from three primary
concerns of plant users [66]:
• Economics - Organizations have long realized that even
though the cost of acquiring an asset (plant) is a fixed cost,
the cost of ownership of this asset throughout its life cycle
(LCC) can vary a lot, therefore in assessing the reliability of
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that asset, the cost of ownership such as maintenance and
operating must be taken into consideration.
Perhaps the most important economic parameter to be
derived is the plant availability, which is the proportion of
time which a component, equipment or a system is capable
of performing its duty, whether it is running or on standby.
The cost of maintenance and operation of the plant depends
on the prediction of the availability and the behavior of this
plant [67].
• Safety - Safety assessment is to uncover all the possible
combinations of fail to danger events and the probability of
their occurrence [68]. The most important parameter is the
likelihood of certain unwanted events occurring on a plant in
a given time [66]. This will help to determine the cost of the
increased safety by assessing the required number of
additional redundancies.
• Project Viability - Reliability assessments can also provide
a comprehensive picture of the feasibility of the project to
the decision-makers regarding the return on investment,
technical feasibility, in addition to reliability and safety
issues.
Reliability assessments are carried out in the early stages of the
conceptual design stages [69, 70], while on existing plants it can be
carried out at any time [71].
2.3.2.1 Analytical models Systems usually comprise of more than one component,
sometimes numbering thousands, and in this sense equipment can
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also be called a system. In reliability, particularly the definition system
means “Repairable” [9, 72-76]. These components are connected with
each other either by series, parallel or mixed in a complex manner.
2.3.2.1.1 Basic principles of probability based Over the years many system models have been developed which
use the principles of probability theory. Examples of these models are
the so-called “point process” or “counting process” models, which are
used for respective failure processes [77]. They are informally defined
as ‘model for randomly distributed events and having a negligible
duration. They are stochastic processes. i.e. [N (t),> 0] with state
space Z=(0,1,….). Such models like Homogeneous Poisson process
[78-80] if it has homogeneous increment.
Renewal process (RP) is another model. Renewal process is a
generalization of the Poisson process [81]. This class of processes is
used to model independent identically distributed occurrences, the
name renewal process is motivated by the fact that every time there is
an occurrence the process “starts all over again”, it renews itself [82].
It also corresponds to perfect repairs [83].
A subset model of the RP is the Generalized Renewal process or
(GRP) introduced by Yanez et al [84]. Yanez demonstrated that unlike
the normal RP, which can only be applied to the AGAN, and ABAO,
the GRP can be applied to the five states of a system after repairs.
These states are:
• as good as new; • as bad as old; • better than old but worse than new; • better than new; and • Worse than old.
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These last three are more commonly encountered in real life.
The Weibull distribution is used extensively due to its flexibility in
modeling the three phases by the change of the shape parameter [9,
72, 85-89] and many others. Normal distribution and exponential
distributions have also been heavily exploited [90, 91].
Block Replacement models assume the items are replaced at
times, i =1, 2 … and s >0, and at failures. The preventive replacement
occur at regular predetermined intervals at a cost of c, whereas
failures within the intervals incur a cost of c + k [92-94]. An extended
block replacement policy with used item was proposed by Sheu [95].
Under such a policy, an operating system is preventively replaced by
new ones at times kT (k = 1,2,3,…) independently of the age and the
state of the system. If systems fail in [(k — 1), T, kT — δ), they are
either replaced by new ones or minimally repaired, and if in [kT — δ,
kT] they are either replaced by used ones or minimally repaired.
Sequential Preventive models was first developed for a single
unit system [96] and was first introduced by Nguyen [97], and has
been since extended to complex repairable systems [98-102].
Many other models were developed based on the basic
probability theory, discrete-time Markov renewal processes [103], The
non arithmetic estimation and consistency for renewal processes [104],
the Markov renewal approach [105, 106], some of them were
developed for the stand by or redundant systems [107-110], some are
for special cases [111-113], this is due to the fact of the complexity of
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the mathematics involved, which makes it very hard to use for general
applications.
2.3.2.1.2 Markovian theory In 1907 a Russian mathematician, A. A. Markov (1865-1922)
introduced a special type of stochastic process whose future
probabilistic behavior is uniquely determined by its present state. That
is its behavior is non-hereditary, or without memory. A variety of
physical systems falls within this category [44].
A Markovian stochastic process with discrete state space and
discrete time space is referred to as a Markov chain. If the time (index
parameter) space is continuous, then it’s referred to as a Markov
process.
The Markov property states that only the present state gives any
information of the future behavior of the process. Knowledge of the
history of the process does not add any new information. In probability
theory, a stochastic process has the Markov property if the conditional
probability distribution of future states of the process, given the present
state and all past states, depends only upon the present state and not
on any past states, i.e. it is conditionally independent of the past states
(the path of the process) given the present state. A process with the
Markov property is usually called a Markov process, and may be
described as Markovian [40, 114]
To use the Markov modeling method in reliability a certain
assumptions has to be made [115];
a. The probabilities of a transition occurring in small time
interval ∆t from system state i to sate j is Θ ∆t, where Θ is a
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constant. This constant takes the dimension pf the
occurrences per unit time.
b. All occurrences are independent.
c. The transition probability of more than one transition
occurrences in a small time ∆t is slight and neglected.
With these assumptions and the availability of failure rate or
repair rates of such equipment, the values of availability and
unavailability of repairable systems can be obtained [116-122].
Bukowski and Goble [123] used it for safety analysis of programmable
electronic systems, Csenki [124] used the semi Markov to model the
interval reliability of systems, Gross [125] used it for obtaining the
steady-state probability distributions of Markovian multi-echelon
repairable item inventory systems.
2.3.2.1.3 Bayesian theory Bayesian theory and Bayesian probability are named after
Thomas Bayes (1702 — 1761), who proved a special case of what is
now called Bayes' theorem. The term Bayesian, however, came into
use only around 1950, and it is not clear that Bayes would have
endorsed the very broad interpretation of probability that is associated
with his name [40].
True Bayesians actually consider conditional probabilities as
more basic than joint probabilities. It is easy to define P(A|B) without
reference to the joint probability P(A,B). This can be seen by re-
arranging the conditional probability formula to get:
P(A|B) P(B) = P(A,B) 2-1
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but by symmetry we can also get:
P(B|A) P(A) = P(A,B) 2-2
It follows that:
2-3
Which is the so-called, Bayes Rule.
Lifetime or repairable reliability population models have one or
more unknown parameters. The classical statistical approach
considers these parameters as fixed but unknown constants to be
estimated. Hence probability statements cannot be made about the
true parameter since it is fixed, not random, since a confidence interval
for an unknown parameter is a frequency statement about the
likelihood that numbers calculated from a sample represent the true
parameter. Bayesian theory treats these population model parameters
as random, not fixed quantities.
There are some assumptions to be made prior to using the model
for reliability evaluation:
• The failure times of the system can be adequately modeled by
the exponential distribution. For a repairable systems, this
means the HPP model applies and the system is operating in
the flat portion of the bathtub curve.
• The MTBF for the system can be considered as chosen from a
prior distribution model that is an analytic representation of
previous information or judgments about the system's
reliability. The form of this prior model is the gamma
distribution (the conjugate prior for the exponential model).
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The prior model is actually defined for λ = 1/MTBF since it is
easier to do the calculations this way.
• Prior knowledge is used to choose the gamma parameters
a, and b for the prior distribution model for λ.
Carnero [126] used it as a strategic decision for setting up of a
Predictive Maintenance Program, Guida [127] used Bayesian
procedure, to develop a model based on prior information on model-
free quantities, in order to allow technical information on the failure
process to be incorporated into the inferential procedure and to
improve the inference accuracy, El-Gohary [128] presented a three
parameters in a three state semi-Markovian reliability model with
maximum likelihood. Mazzuchi [129], presented a theoretic approach
model for determining optimal replacement strategies. The advantages
of Bayesian methodology:
• Uses prior information.
• If the prior information is encouraging, less new testing
may be needed to confirm a desired MTBF at a given
confidence.
• Confidence intervals are intervals for the (random)
MTBF.
But the disadvantages of it are:
• Prior information may not be accurate - generating
misleading conclusions
• Way of inputting prior information (choice of prior) may not
be correct
• Customers may not accept validity of prior data or
engineering judgments.
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• There is no one "correct way" of inputting prior information
and different approaches can give different results.
• Results are not objective and do not stand by themselves.
2.3.2.1.4 Poisson process A Poisson process, named after the French mathematician
Siméon-Denis Poisson (1781 - 1840), is a stochastic process which is
defined in terms of the occurrences of events. This counting process,
given as a function of time N(t), represents the number of events since
time t=0 [40]. Poisson process is a kind of a Markov process [130] and
known for its use for modeling the number of events that are occurring
within a given time interval.
The Poisson distribution has two main applications in reliability
[72], firstly for describing the number of events (e.g. failures) in a
specified interval of time and secondly as a useful approximation for
the binomial distribution when the binomial parameter P is small, and
the Poisson based model assumes that failure probability of a system
follows the Poisson distribution and the number of failures does not
effect the failure probability and the repair does not change the
reliability of the system [131].
Perhaps the most recognized application of the Poisson process
is the Duane AMSAA (DA) model for monitoring the reliability growth
[132, 133], the model is based on the assumption that the failures are
the result of a non homogeneous Poisson process (NHPP), i.e. the
number of failures in specified time interval have a Poisson distribution
but the failure rate is non constant. Shiang [134] Huang used a non-
homogeneous Poisson process (NHPP) with a power-law intensity
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function to present ideas on the applications of fuzzy concepts to
decision making for deteriorating repairable systems.
2.3.2.1.5 Models based on the Condition monitoring data The most preferred type of maintenance currently is the condition
based maintenance (CBM) as it is a proactive type of maintenance and
provides an early warning if the equipment condition is deteriorating
through the potential failure to failure (P-F diagram), which (depending
on the fault and type of equipment) provides an early warning for the
maintenance personnel to take the appropriate action. Preventive
maintenance can be made more efficient by periodic monitoring
wherein the state of deterioration can be assessed [135].
To increase the reliability of plants, many models have been
developed based on the availability of data through condition
monitoring techniques and equipment, Pan et al [136] developed a
support vector data description (SVDD) model which is a single
classifier and it can distinguish the normal and fault condition just using
normal samples and compared it with a neural network (ANN). Wang
[137] developed a probability model to predict the initiation point of the
second stage and the remaining life based on available condition
monitoring information. Sun [138] proposed proportional covariate
model (PCM) to overcome the difficulty of predicting the hazards of
mechanical systems accurately, and demonstrated that this new
approach to hazard estimation can reduce the number of accelerated
life tests significantly. Christer [139] introduced a replacement action
decision aid for a key furnace component subject to condition
monitoring by developing A state space model to be used to predict
the erosion condition of the inductors in an induction furnace in which a
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measure of the conductance ratio (CR) is used to indirectly assess the
relative condition of the inductors, and to guide replacement decisions.
2.3.2.2 Other techniques 2.3.2.2.1 Condition monitoring and fault diagnosis (CMFD)
The condition monitoring and fault diagnosis CMFD is perceived
as the new generation in the practices of maintenance management.
With the Higher costs of outages of plants due to the increases in the
complexity, cost, high levels of automation and tighter profit margins
and the increased level of safety awareness have all warranted the
advances in CMFD encouraged by the technological advances in the
condition monitoring techniques [140], many techniques exist for
condition monitoring , but they all have 2 concepts in common; a
condition data acquired, interpreted and made available and an
appropriate action taking accordingly. Condition based maintenance
(CBM) or On-condition maintenance both are relying on condition
monitoring concept and techniques to initiate action [141-145].
Today there many condition monitoring techniques but they all
fall under one of these categories [9]:
• Dynamic effects
• Particle effects
• Chemical effects
• Physical effects
• Temperature effects
• Electrical effects
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All of the CM techniques are aimed at identifying the (P-F)
interval to give an early warning so the maintenance department can
make a decision on what action should be taken and when. Some of
these techniques are [9]:
• Dynamic monitoring, such as Vibration analysis, Real time
analysis, Peak value analysis, Spike energy.
• Particle monitoring, such as ferrography, all metal debris
sensors, magnetic chip detection, graded filtration.
• Chemical monitoring, such as exhaust emission
analyzers, moisture monitor, crackle test, clear and bright
test.
• Physical effects monitoring, such as Liquid Dye
penetrates, magnetic particle inspection, ultrasonic.
• Temperature monitoring, such as infra red scanners,
temperature indicting paint.
• Electrical effects monitoring, such as power factor
testing, electrical resistance, motor circuit analysis, meggers
and other voltage generators, and many more.
2.3.2.2.2 Fault tree and root cause analysis The root cause analysis (RCA) is a problem solving method used
to find the true cause of a problem. The practice of RCA is predicated
on the belief that problems are best solved by attempting to correct or
eliminate root causes, as opposed to merely addressing the
immediately obvious symptoms. By directing corrective measures at
root causes, it is hoped that the likelihood of problem recurrence will
be minimized [40], root cause analysis is not a single, sharply-defined
methodology; there are many different tools, processes, and
philosophies of RCA in existence. However, most of these can be
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classed into five, very-broadly defined "schools" that are named here
by their basic fields of origin: safety-based, production-based, process-
based, failure-based, and systems-based. Root cause analysis
techniques
• 5 Whys
• Barrier analysis
• Change analysis
• Causal factor tree analysis
• Failure mode and effects analysis
• Ishikawa diagram, also known as the fishbone diagram or
cause and effect diagram
• Pareto analysis
• Fault tree analysis
• Bayesian inference
Fault Tree analysis is one of the most widely-used methods in
system reliability analysis. It is a deductive procedure for determining
the various combinations of hardware and software failures, and
human errors that could result in the occurrence of specified undesired
events (referred to as top events) at the system level. A deductive
analysis begins with a general conclusion, then attempts to determine
the specific causes of this conclusion. This is often described as a "top
down" approach [146]- [73, 147, 148].
2.3.2.2.3 Reliability block diagram (RBD)
A Reliability Block Diagram is a form of reliability analysis using a
functional diagram to portray and analyze the reliability relationship of
components in a system. Each element of a system shall be
represented by a block that is in some way interconnected with or
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through the other blocks of the system at a desired level of assembly
[149].
It can be used to facilitate the assessment of overall system
reliability, and the connections between the blocks symbolizes the way
in which the system will function as required and not necessary how
the actual physical parts are connected, that is why it is sometimes
called the success diagram method (SDM). It is considered the first
step in analyzing the system reliability [150], which looks at the logical
interdependencies (parallel or series paths) required for the system
under analysis to function correctly, and it is more suitable for
quantitative analysis as it can calculate the exact reliability of the
system at a given time [38].
2.3.2.2.4 Failure mode, effects and criticality analysis (FMECA) FMECA is a procedure that is used to analyze failures (failure
modes) and determines their effect at both the local and system
levels. The analysis can be carried out from the lowest to the highest
level of the system (bottom up), which is commonly referred to as a
hardware analysis. Alternatively, the analysis can be carried out from
the highest level to the lowest level (top down) of the system, which is
commonly referred to as a functional FMEA. The functional FMEA
considers the functional failure of components within a system.
FMEA is applied in maintenance tasks, such as reliability-
centered maintenance (RCM) and risk-based maintenance (RBM).
The effects are generally classified as operational (production),
environmental, and safety effects.
This procedure is used to plan tasks to find minimum ratio
between maintenance cost and cost due to failure effects. FMECA is a
structured method to determine equipment functions, functional
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failures, and assess failure causes and effects [19, 42, 151]. FMEA
outputs sometimes used as inputs to a higher level fault tree analysis,
its use is limited by the time and resources available and the capacity
to derive a sufficiently detailed database [66].
Wang [152] developed a model that can be used to identify all
possible system failure events and associated causes, and to assess
the probabilities of occurrence of them particularly in those cases
where multiple state variables and feedback loops are involved by
combining FMECA and the Boolean Representation Method (BRM).
Tao et al [153] combined FMECA With fault tree analysis to asses the
reliability of a redundant actuator system.
2.3.2.2.5 Monte Carlo simulation The name “Monte Carlo'' was coined by Metropolis (inspired by
Ulam's interest in poker) during the Manhattan Project of World War II,
because of the similarity of statistical simulation to games of chance,
and because the capital of Monaco was a center for gambling and
similar pursuits [154]. Monte Carlo methods are algorithms for solving
various kinds of computational problems by using random numbers (or
more often pseudo-random numbers), as opposed to deterministic
algorithms. It can be used in reliability to evaluate the system
reliability and availability [148], where a logical model of the system
that is being analyzed is repeatedly evaluated, and each run uses
different values of the distributional parameters. Its considered an
attractive alternative approach when it comes to express the [155, 156]
complicated relationship among various parameters of the model, and
its input algorithms are easy to understand and there are no
constraints in regards to the nature of the input assumptions on the
parameters such as repair rates and failures.
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In this study, we will be using the Monte Carlo simulation
extensively to model the system and to optimize it in conjunction with
fault tree analysis, as there were many successful researches used
this combination for system analysis and optimization [157-163], we
will also use the method in conjunction with reliability block diagrams,
as it’s a good way for representing the dependencies of the system,
reliability and availability [164-172].
2.3.3 Maintenance optimization Maintenance optimization is the discipline within operations
research concerned with maintaining a system in a manner that
maximizes profit or minimizes cost [173]. Maintenance optimization
strategies are often constructed by using the stochastic models by
concentrating on finding the optimal time or the optimal acceptable
degree of system degradation before maintenance and/or replacement is
implemented. Normally it’s done under these categories, cost based
optimization, risk based optimization, or combined optimization policy
[38].
2.3.3.1 Cost based optimization policies All maintenance activities are carried out for merely one reason,
which is to reduce or minimize the overall cost of operations, and
industrial plant availability and economics strongly depends on the
maintenance activities planned [174].
The cost-based approach to maintenance planning was originally
developed by Jardine [175], he assumed that the overhaul will return
the equipment to the as-good-as-new condition and that the failure
repair between preventive maintenance actions makes it possible to
run the machine up to the next interval (i.e., it results in a bad-as-old
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condition). He also estimated that the optimal interval between
preventive replacements of equipment subject to breakdowns, and
may be applied to preventive maintenance.
Many attempts have been made to optimize the maintenance
tasks based on the cost factor, Kenne [176] claims to have developed
a model for the joint determination of an optimal age-dependent buffer
inventory and preventive maintenance policy in a production
environment that is subject to random machine breakdowns.
Bris [177] demonstrated an efficient simulation algorithm for the
quantification of reliability performance indicators of a complex system
that is based on Monte Carlo method by introducing a cost-
optimization problem which may be fully solved by the algorithm using
additional genetic algorithms as an applicable optimization technique,
while Barata [178] used Monte Carlo simulation to model continuously
monitored deteriorating systems and embedded the resulting model
within an ‘on condition’ maintenance optimization scheme that aims at
minimizing the expected total system cost over a given mission time.
Opportunistic maintenance is more favorable over PM, especially
in continuous operation environments as this means the equipment
does not have to go off-line in order for the maintenance task to be
performed [21, 96, 179-181].
2.3.3.2 Risk based optimization policies Conventional reliability analyses have been oriented towards
selecting the more reliable system and preoccupied with maximizing the
reliability of engineering systems [182]. Risk based optimization is taken
when the failure can endanger the life of the operator, customer,
community or the environment and linked with the losses from failures.
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The main concerns in this field are the safety and protective
devices and stand-by equipment, and usually carried out during the
design stage [183-186].
2.3.3.3 Other Maintenance strategies A number of other strategies have been developed such as;
• Business–Based Maintenance (BBM): based on determining
the production process requirements in conjunction with
maintenance activities, and specific to emergency
maintenance, this was developed based on the business
centered maintenance (BCM) by Siemens.
• Preventive Maintenance Optimization (PMO): this strategy is
aimed at continuously reviewing and updating the
maintenance tasks based on failure history, changes in
business objectives, and the emergence of new
methodologies and techniques.
• The Relative Condition Parameter (RCP): a policy that
depends on the sophistication of condition monitoring
devices to take theses factors into account [38].
• Delay Time Modeling (DTM): this strategy optimizes
maintenance by using routine inspection and other condition
monitoring techniques to identify the time at which a defect is
originated, and understand the physics of the defect in order to
predict total failure, and therefore optimize maintenance
intervals [6].
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2.3.3.4 Discussion The growing pressure of modern world, with the changing of the
business objectives, increased awareness of safety and environmental
concerns, the stiff competition to survive or capture a greater share
market and the modernized and complicated technology have put
maintenance on the focus to increase the reliability and availability of the
equipment and systems to meet these demands, which in turn has led to
an increasing interest in the development and implementation of optimal
maintenance models or strategies for improving system reliability,
preventing the occurrence of system failures, and reducing maintenance
costs of deteriorating systems.
Those models were classified in different types of classifications,
and the main aim of these classifications was to give the researchers
and the practitioners’ guidance, so that they can recognize the model
that best fits their maintenance needs.
Maintenance objectives in general sense is to improve system
availability and MTBF, reducing failure frequency and downtime.
However, since maintenance incurs cost, to reduce maintenance cost
is also necessary. Most researches in maintenance were aimed at
studying the stochastic behavior of systems under various
maintenance policies, and to determine the optimal maintenance
policies. The stochastic behavior of systems is mainly represented by
system maintenance cost measures, these measures are:
maintenance cost rate, discounted cost rate, and the system reliability
measures: availability, MTBF and failure frequency, etc. General
speaking, optimal system maintenance policies can be one of the
following:
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A policy that can minimizes the system maintenance cost
rate,
A policy that can maximizes the system reliability
measures,
A policy which can minimizes system maintenance cost
rate while maintain the requirements of the system
reliability requirements to a satisfying level.
A policy which can maximize system reliability
parameters when the requirements for the system
maintenance cost are met.
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CHAPTER THREE - FIBERBOARD PRODUCTION PROCESS 3. 3.1 Introduction
There is no precise number of how many wood pulp mills around
the world, as they are classified under different naming and categories,
or according to the type of product that they produce, but they all have
more or less the same concept of production process, a rough
estimate was done in the early nineties showed that there are about
over 1300 mills and the number is climbing. In searching the literature,
the author found very few articles about the hardboard industry [187-
190], all these articles does not address any reliability or optimization
of the system that we are going to analyze.
Some papers presented at the annual pulp and paper
maintenance and reliability conference in the US [191] annually, but all
of them do not go to specific details of the reliability and maintenance
program, rather they speak about their general experience in
implementing these programs and the achieved results.
3.2 The Hardboard production process The Masonite board production uses wood chipping mix as the
ingredient, the source of the wood chipping is from:
a. Wood saw mills waste (80%).
b. Wood logs (20%).
This percentage varies depending on availability, price and
sometimes the produced product.
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3.2.1 Wood chipping and mixing Both forms of the wood are shipped into site and stored in the
wood yard. The logs are fed into wood chipper to transform the logs
into chips with the exact size that is suitable to produce the board. The
produced chips are then stocked in the wood yard.
The chips are then fed by front-end loader into a feeding bin
according to the mixing % indicated above. It’s then fed through series
of conveyors to chips feeding bins then through to steam heaters and
then fed to the defibrators.
3.2.2 Pulp preparation and storage stage The defibrators are machines that break the chips into their basic
fibers using steam pressure supplied from a coal fired boiler and
crushing discs called segments. The defibrators are adjusted so to
produce the required quality of wet fiber Depending on the type of
board to be produced.
Wet chips are then fed into the cyclones, which mix the fibers
with water to produce pulp, which drops into no.1 stock chest.
A sample of pulp is taken from the cyclones using a spoon like
tool; water is squeezed out of pulp and then pulled apart. Visually
determine the quality of pulp. Pulp quality for each board caliper
should meet the conditions prescribed in the comments field of the
table.
Stock chests are large concrete containers some of them are
built underground and some above ground with an agitator.
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From stock chest no1 the pulp is transferred by pump to a
dewatering screw to drain the pulp from water and to a re-pulper, while
the water goes to Brown water chest, the pulp from the pulper is then
transferred to stock chest no 3 and from there to the Raffinators.
Stock chests are large storage containers that holds the pulp
stock, contaminated water from the process and process-recycled
water are held in, these chests are:
a. Stock chest no 1
b. Brown water chest
c. Stock chest no 3
d. White water chest
e. Stock chest no2
f. Machine chest
The Raffinators are machines that refine the pulp further than
what it is.
A sample of pulp is taken from the Raffinators using a spoon like
tool; water is squeezed out of pulp and then pulled apart. Visually
determine the quality of pulp. Pulp quality for each board caliper
should be as described above.
The pulp is then transferred to the machine chest.
3.2.3 Pulp forming stage From the machine chest the pulp transferred by pump to the
forming machine and will by circulated via the machine chest pump
until it reaches the desired consistency and then pumped by the same
pump to the former after closing the re-circulating valve. Filtered white
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water from the process also added to the pulp as it leaves the machine
chest.
At the former the pulp is laid on fine mesh endless conveyor
called the wire to drain the pulp from water, at half the distance the
overlay pulp is introduced on top of the main pulp matt that has been
formed. The overlay is Part of the pulp that comes into the former is
drawn and used as an overlay. This pulp is laid on the main pulp
formed matt.
The drained water from the wire conveyor drops into the wire pit
and to the white water chest.
There are also 2 vacuum boxes under the wire to suck the water
from the pulp by a vacuum pump, the sucked water then transferred
into a tank and then filtered and cooled and reused to flush the lines
and as a gland water for the pumps and agitators bearings.
From the wire conveyor the pulp matt is pressed through series
of rolls to drain it from water and to give it the required bond to travel
through the line, cut into the required length and width.
3.2.4 Sizing stage After the relatively dry pulp leaves the former as a continuous
matt its sides will be trimmed by side cutters to give it the strait edge
required. The cut offs from the matt drops into a pit called the broke pit
that has an agitator to form it back to a pulp and return it to stock chest
no 2.
After the side cutter the matt is cut to the required board length
by a side cutter and by now the matt is called wet mat.
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3.2.5 Wet mat transfer stage (plate circuit) This board travel on conveyor to another conveyor where it
reverses its direction to take it back under the first conveyor and place
it on the board plate. The board plate is a stainless steel sheet or plate
with a stainless steel mesh on top of it to help drain the residual water
from the board as it gets pressed in the press.
3.2.6 The pressing stage The plate then travels into the press loading hoist, which stacks
the plates at fixed distance on top of each other. It has 30 racks.
After the loading hoist is full there is a pusher called the wire
pusher pushes the plates into the press, the press also has 30
compartments or openings called daylights.
Each compartment comprises of wear plate, surface plate and
heating platens. Each platen has a heating medium (hot water)
charged into the platens through 2 vertical manifolds. The hot water is
at 180°C and is circulated from the boiler; this is to help initially cure
(dry) the board during the pressing process.
The pusher returns to its original position after placing the plates
(with the wet board on top) on top of the platens and the press cycle
starts.
The press is controlled by CITECT SCADA system. The cycle
starts with four hydraulic cylinders raise up pushing the platens
together, the hydraulic used is mixture of hot water and oil emulsion
and the desired pressure is generated and reached by means of the
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accumulator, hydrauphore tank and series of low and high pressure
pumps located at the pump house.
The press has an upper firm and a lower movable press tables
both of them cast in one piece of first rate steel casting. Between the
press tables there are located 31 heating platens forming 30 openings,
into which the wet fiber boards coming from the loading hoist.
Press cycle time depends on the product and desired caliper and
can last up till 10 minutes. The pressing act drains the rest of the water
from the board, while the heat form the platens cure the board.
A more detailed explanation of the press function will be
discussed in a later section and the working pressures and
temperatures will be discussed in the products types.
After the pressing cycle is completed the press rams goes down
and the unloading hoist extract the boards out from the press. The
boards then unloaded from the board plates, the plates return to the
plate circuit for another cycle and the board travels on conveyors to the
loaders where it’s loaded in to the loading trucks and driven to the
tempering chamber.
3.2.7 Tempering stage The tempering chambers are fitted with heating coils with hot
water running through them and a fan fitted behind the coils to produce
hot air to cure the boards and to dry them.
Again the operating temps and time boards stay in the tempering
chambers depends on the product.
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3.2.8 Humidification stage The trucks are then exit the tempering chambers and enter the
humidifying chambers where steam is introduced to the boards to bring
its moisture contents to about 7% to enhance the board properties.
Again the operating temps and time boards stay in the
humidifying chambers depends on the product.
3.2.9 Grading and secondary process
After the humidifying chambers the trucks travels to the Anthon
saws area and unloaded. Depend on the products required the boards
are either travel to the grading bins to be sent to the saws to be cut
according to the orders or they travel to the planer.
The planer is a machine that a grinds a layer from the board back
to get it to the desired thickness according to customer’s demands and
then stacked and cut to size and then strapped and sent to the
warehouse.
Figure 8: A schematic diagram of the production flow process
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3.3 System selection Before we embark on the process of system selection, we
illustrate the process of the research. Figure 9 illustrates the research
process.
Figure 9: The research process flow chart
The first step is the system selection, as we are going to target
one area (stage) of the production line in this research. The selection
criteria will be based on the highest downtime and its criticality to the
production process.
A simple and effective method of selection is the histograms,
whereby extracting the daily downtime of the production areas or
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stages, accumulate them and tabulate them and present them by
means of flow chart in the form of histograms.
Figure 10 shows the downtime in each area of the plant over a
period of one year. The chart indicates that the pressing plant has the
highest downtime; therefore this area will be targeted in this research.
This is in line with the fact that the press plant is the most important
part of the process, as in the press the transformation of the pulp into
board occurs, plus it represents the bottleneck of the process, which
led the company to increase the capacity of the press from twenty five
openings to thirty, this design change did increase the capacity of the
press, but still didn’t solve the bottle neck problem completely as the
problem remains with pressing cycle time.
Lost time breakdown by processes areas
0100200300400500600700800900
100011001200
Pulp
pre
p
form
er
Wet
lap
Plat
eci
rcui
t
Pres
s
Plat
e &
scre
ens
Boa
rdco
nvey
ors
Load
ers
Hea
ters
Ant
hon
saw
s
Process Area
Hou
rs
Figure 10: Breakdown of the plant areas downtime
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3.4 An overview of the selected system (Pressing plant) The press plant is used for transforming the wet pulp into dry
board, by draining the moisture out of the pulp by means of
compression and heat. The production of wood fiber by mixing wood
chips with water is known as the wet method.
3.4.1 Press Technical Data: Press power about 4,500 Tons
Number of press cylinders 4
Hydraulic working pressure 4200psi
Diameter of press rams 700mm
Number of heating platens 31
Number of light openings 30
Size of heating platens 5700mm x 1500mm
Opening between heating platens 85mm
Active pressure surface 5580mm x 1430mm
Pressure on active press surface 782psi
3.4.2 Press technical description The press has an upper firm and a lower movable press tables,
both of them cast in one piece of steel casting. Between the press
tables there are located 31 heating platens forming 30 openings, into
which fiber boards coming from the loading hoist are introduced by
means of a charging device called the pusher.
The press originally was designed for 25 openings but extra
openings were added 6 years ago.
On pressing the movable press table is lifted by dint of four
hydraulic cylinder units, each of which being connected to the upper
press table by means of two columns.
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The press cylinders, which are made of first rate steel casting,
are provided with exchangeable wearing liners (plates) - a feature of
great importance when its necessary to undertake a reconditioning of
the press due to wear. The packing compartment is easily accessible
by a removable gland ring and is amply dimensioned for enabling
appliance of various types of packing.
The rams sliding in the cylinders bores are made of chilled
special casting and are, therefore, very resistant both to wear and
chemical attacks. Besides the surface of the rams are grounds to high
surface finish.
The columns which owing to the heavy and varying load are
particularly strained components, are made of first- rate forging.
The heating platens are built up of solid rolled steel plates in
which a system of channels is drilled for the heat medium, which can
be steam or hot water; they have one inlet and one outlet each for the
heat medium. This necessitates only one inlet manifold and one outlet
manifold located at the ends of one of the length sides of the press at
the same time as the amount of link pipes for connection to the heating
platens is reduced to a minimum.
The pressure liquid required for the operation of the press is
mixture of hot water and emulsion oil and is obtained from a pump
plant mainly consisting of 2 low pressure pumps, 1 air loaded
accumulator and 4 high pressure pumps along with the necessary
valves and controls.
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3.4.3 System functionality The press cycle is divided into 3 stages according to the working
pressures WP1, WP2 and WP3.
Figure 11: Schematic drawing of the press
After the loading hoist is loaded with wet boards, the press doors
which is used to protect the wet boards on the loading hoist from the
effluent leaving the boards that being pressed is opened, and the
pusher move forward inside the loading hoist and push the wet board
plates into the press. The pusher then returns to its original position
and the door is closed and the pressing cycle or WP1 starts.
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Figure 12: Block diagram of the pressing process
• Working Pressure stage 1
The pressing cycle starts with the operation pressure
liquid first runs from the air pressurized accumulator tank
(4), the low pressure pumps (2) and the high pressure
pumps (1) (which in turn take the pressure fluid from the
hydrauphore tank) to the press cylinders, when the press
rams moves upward they squeeze the wet boards to drain
them from water, this water runs into the press sump and
discharged by the press sum pump to the water treatment
plant to be treated before releasing it into the drains.
When the press is starting to close the compression of
the fiber board is started. When the accumulator has been
discharged, it is automatically disengaged. The low
pressure pumps starts and continues to close the press, till
it reaches to its maximum pressure and is then
automatically disengaged and starts charging the
accumulator tank. Then the high pressure pumps start to
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complete the closing of the press and developing the
working pressure up to a value of 3800 Psi.
When this has been reached , a changing over of the
high pressure pumps follows, which then goes idling, until
the pressure possibly commences to drop, when it is
automatically connected again in order to keep the pressure
at the desired value.
When the set time period for this stage ends the
pressure is released till it reaches the Working Pressure
stage 2
During the press cycle the heating platens are charged
with hot water continuously to help dry the pulp to transform
it into dry board.
• Working Pressure stage 2 The second stage starts when the press pressure
drops to 1000Psi allowing the moisture still trapped in the
board to vaporize and exits the board. By the end of this
stage the board can theoretically be exited out from the
press but it goes to a 3rd stage.
• Working Pressure stage 3 This stage is to consolidate the board strength and as
a cooling off period before the board can finally exits the
press. This happens by reversing the flow of the operating
pressure fluid back to the accumulator tank and the presses
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drops down by its own weight opening the daylights and
have them ready to be extracted.
The boards are then extracted by a device called the
extractor, which has a gripper that grabs onto the carrier
plates tongues and pulls them into the unloading hoist,
which has the same mechanism as the loading hoist but in
reverse function.
Figure 13: Illustrates the press cycle working pressures
3.5 System boundaries definition Before start analyzing the system we need to define the
boundaries of the system, this will enable us to have a clear picture
about the components of the system and to ensure that the
uncertainties associated with the collected data are minimized.
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Figure 14: illustrates the pressing plant and its components (Sunds defibrators-
Sweden)
We will be concentrating on the mechanical aspect of the system.
Figure 15: A schematic diagram of the system boundaries
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Figure 14 shows an illustration of the pressing plant, while Figure
15 shows a system boundary definition.
The system consists of the following:
• Oil-water emulsion storage tank
• Low pressure pumps
• High pressure pumps
• Hydrauphore tank
• Air compressor
• Hot water boiler
• Pressing machine
• Control and flow valves
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CHAPTER FOUR - SYSTEM RELIABILITY ASSESSMENT 4. 4.1 Introduction
After selecting the system to be targeted for the research, we will
perform an assessment of this system in order to evaluate its reliability,
its functional failures, establish the relationship between its
components, so that we can collect and process the necessary data to
build a model of the system and analyze its availability, to be able to
optimize it.
4.1.1 Functional Failure Mode, Effect and Criticality Analysis (FMECA) As mentioned earlier in the literature review, FMEA is a
procedure that is used to analyze failures (failure modes) and
determines their effect at both the local and system levels. Functional
FMEA considers the functional failure of components within a system,
its outputs sometimes used as inputs to a higher level fault tree
analysis, its use is limited by the time and resources available and the
capacity to derive a sufficiently detailed database.
The objective of a FMECA is to allow for the identification of all
the available possibilities for both catastrophic and critical failures with
all their criteria, so that they can be minimized, or eliminated, as early
as possible by the relevant preventative maintenance interventions.
At any level of sophistication, the Failure Mode, Effects and Criticality
Analysis (FMECA) have to contribute significantly to the decision
making process regarding the maintenance program that is being
implemented.
The recommended action will be added after optimizing the
system and selecting the optimal strategies.
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Appendix 12 shows the functional FMECA for the plant
4.1.2 Modeling the failure data The objective of reliability data analysis is to construct a
probability model of the failure process. The main reason for building
such models is that they offer scope for predicting and improving the
future reliability performance of equipment or systems. These results
can be very beneficial for assuring the safety and productivity of
installations, and provide input to other associated studies in
maintenance planning, inspection scheduling, spares holdings and
many other activities where the reliability performance of plant,
systems and equipment is of concern [72].
Having defined the system boundaries, we will model the failure
data and fit them into the appropriate distribution and estimate the
reliability parameters.
Calculating the Correlation Coefficient:
The correlation coefficient is a measure on how well the linear
model fits the data. The closer the absolute value is to 1, the better the
fit.
The correlation coefficient is calculated in the Cumulative
Probability distribution (cpd) of every graph by the Weibull program
and is denoted by the symbol (ρ).
The correlation coefficient is calculated from
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⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜
⎝
⎛⎟⎠
⎞⎜⎝
⎛
−
⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜
⎝
⎛⎟⎠
⎞⎜⎝
⎛
−
−=
∑∑
∑∑
∑∑ ∑
=
=
=
−
=
= =
N
i
N
ii
i
N
i
N
ii
i
N
i
N
i
N
iii
ii
N
yy
N
xx
N
yxyx
1
2
12
1
2
12
1
1 1.
ρ 4.1
Where ix and y are the x and y values of points in the
cumulative probability plot. N is the total number of points plotted.
Estimating the Goodness Of Fit (GOF):
Goodness of fit is a measure of how well a statistical
model fits a set of observations. Measures of goodness of fit
typically summarize the discrepancy between observed
values and the values expected.
The goodness of fit (ε) is calculated in the Cumulative
Probability distribution (cpd) of every graph by the Weibull
program and is denoted by the symbol (ε).
The expression below indicates how the goodness of fit indicator
is calculated.
( )2
1
ˆ
N
yyN
iii∑
=
−=ε 4.2
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Where iy and iy are the fitted Weibull unreliability values and
estimated unreliability point values respectively. N is the total number
of points plotted.
Also calculated and denoted by B10, B15, and B20, is the life
where by 10%, 15%, and 20% of component failures would have
respectively occurred. i.e., the times, at which the Unreliability of the
component is 0.1, 0.15, and 0.20 respectively.
Note:
• All the parameters estimated by the Weibull distribution model,
used Median Rank method for estimating these parameters,
unless specified.
• To model the data we will use the Isograph software.
4.1.2.1 Press Rams 4.1.2.1.1 Estimating the MTBF
We obtain the failure data from the downtime database, since the
rams seals are replaced every time they fail, hence it can be
considered complete data.
Table no.1 shows the failure data taken from the database. Table 1: Press rams failure data
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Re-arranging the data and calculating the times to failure (TTF) in
days.
Table 2: Press rams TTF (days)
Plotting the times to failure on Weibull graph and estimating the
reliability data.
Figure 16: Press rams Weibull plot
Also plotting the data and estimate the mean life
From the graphs we get the followings:
µ=390.9 days
η=453.1 days
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β=2.0
The cdf (cumulative density function) at mean life; β
η)(
)( 1t
t eF−
−= 4.3
2)1.4539.590(
)9.590( 1−
−= eF
F(t)=0.52
R(t)=1-F(t)=1-0.52 4.4
R(t)=0.48
MTBF=η[(1+β)/β] 4.5
MTBF=680 days
4.1.2.1.2 Estimating the Mean Time To Repair MTTR MTTR is a characteristic describes the average time to repair a
system with maintainability M(t) and is generally the term used to
represent the average restoration time in quantitative analysis.
Since the repair mode is repacking the ram seals (single repair
mode), therefore we can calculate the MTTR by dividing the total time
taken by no. of repairs.
Table 3: Press rams Repair times (hrs)
repairsofnotakentimetotalMTTR
....
= 4.6
MTTR = 14.6/10 = 1.4 hrs
4.1680680)(+
=+
=MTTRMTBF
MTBFAtyAvailabili 4.7
A=0.997
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4.1.2.2 Solenoid valves S1, S2, S3, S4 and S10 4.1.2.2.1 Estimating the MTTF
Since there are no available data for these components, we will
make use the generic reliability data available from the reliability data
handbook[72].
From the generic tables, the solenoids median failure rate is:
)/(7.5.. mhfratefailureMedian =
66 107.5
107.5 −== xλ
tetR λ−=)( 4.7
)8760107.5( 6
)8760( xxeR−−=
951.0)( =tR
)(1)( tRtF −=
049.0951.01)( =−=tF
For non-repairable components
)()( tFtQ = 4.8
lityUnavailabitQWhere =)(.
And;
)(1)( tQtA −= 4.9
A (8760) =1-0.049=0.951
4.1.2.3 Low-pressure pumps 4.1.2.3.1 Estimating the MTBF
We test the data to see if it fits the HPP
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Table 4: Low pressure pumps TTF
Rearranging the failure data to plot the cumulative failures:
Table 5: low pressure pumps Ranked ttf,N(T),log cumulative and log N(T)
Since the graph is a curve, its not HPP
Figure 17: Testing the LPPs data against the HPP
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We test the data against the NHPP by taking the log
Figure 18: Low pressure pumps log TTF-log N(T)
Since it’s a NHPP, we use DA-AMSSA model to determine the
model parameters
The failure intensity function is given by: 1)( −= βλβ ttw 4.10
Where β=
117.3008.1
10log1470log1log12log
−−
=−−
β= 0.5
βλtN
= 4.11
497.0147012
=
λ=0.312
dayfxxxtw /1006.414705.0312.0)( 315.01470
−− ==
= 4.06x10³ f/day
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MTBF=1/ w(t) 4.12
= 5898 hrs
The instantaneous failure rates refers to the population of 2
pumps, therefore at 1470 days, the MTBF is
MTBF= 2x 5898=11796 hrs
))(
(1)( xM
t
x etF−
−= 4.13
)117961470(
)( 11470
−−= eF t
117.0)( 1470=tF
R(t)=1-F(t)
R(t)=1-0.11=0.883
4.1.2.3.2 Estimating the MTTR Rearranging the repair data to plot the repair model and truncate
them Table 6: Low pressure pumps repair data
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We plot the repair data and obtain the MTTR
Figure 19: Low pressure pumps repair distribution graph
For lognormal distribution
2
21σµ +
= eMTTR 4.14
From the graph, µ= 21.9353 and σ= 149.331
MTTR=9.32 hrs
32.91179611796)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.9992
4.1.2.4 High-pressure pumps 1-4 4.1.2.4.1 Estimating the failure model
Processing the data and checking the process against HPP
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Table 7: HPPs re-arranged failure data
High pressure pumps
0
5
10
15
20
25
30
0 500 1000 1500TTF
N(T
)
Figure 20: Testing the High Pressure Pumps 1-4 data against the HPP
Since it’s a curve, we test for NHPP, by taking log of the values
Figure 21: HPPs 1-4 log failure data
Using DA-AMSSA model, we determine the model parameters
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1)( −= βλβttw
Where
46log227log1log15log1
−−
=β
66.136.2018.1
−−
=
β1= 1.68
251log1500log16log23log2
−−
=β
4.218.32.136.1
−−
=
β2= 0.2
βλtN
= 4.15
68.1227151 =λ
λ1=0.0016
2.01500232 =λ
λ2=5.32 The estimated instantaneous failure rates at 251 and 1500 days
are: 168.1
227 22768.10016.0)( −= xxtw
= 0.1 f/day
MTBF=1/ w(t)x24
=223.2 hrs 120.0
1500 150020.032.5)( −= xxtw
= 0.003 f/day
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MTBF=1/ w(t)x24
= 7824 hrs
The instantaneous failure rates refers to the population of 4
pumps, therefore at 227 days, the MTBF is
MTBF= 4x 223.2=892.8 hrs
And at 1500 days
MTBF=4x7825=31296 hrs,
Because this is the last value, therefore we will use this value in
the analysis
)312961500(
)( 11500
−−= eF t
046.0)( 1500=tF
R(t)=1-0.04=0.953
4.1.2.4.2 Estimating the MTTR
Re-arranging the times to repair and truncate them
Table 8: HPPs1-4 times to repair
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Using the exponential distribution to estimate the MTTR
Figure 22: HPPs 1-4 repair distribution graph
MTTR=1/µ 4.16
=1/0.52
=1.9 hrs
9.13129631296)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
4.1.2.5 Boiler set 4.1.2.5.1 Estimating the MTBF
Checking the data against HPP
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Table 9: Bolier TBF (hrs) /N(T)
Boi l e r c umul a t i v e f a i l ur e a ga i nst e l a pse d t i me
-5
0
5
10
15
20
25
30
0 1000 2000 3000 4000 5000 6000 7000 8000
Elapsed t ime (t )
Figure 23: Boiler TBF/N(T) graph
Since the graph is curved, its not HPP, and therefore we will
check the data against NHPP
Taking the log of cumulative failures N(T) and the log of TBF and
plotting them
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Table 10: Bolier log N(T), log TBF
B o iler set reliab il it y g rowt h mod el
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.86 1.96 2.06 2.16 2.26 2.36 2.46 2.56 2.66 2.76 2.86 2.96 3.06 3.16 3.26 3.36 3.46 3.56 3.66 3.76 3.86 3.96
Log t
Figure 24: Bolier log t-log N(T)
As the plot is straight line, hence it’s a NHPP
Using DA-AMSSA model, we determine the model parameters 1)( −= βλβttw
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Where β1=
68.19.2015.1
72log792log1log14log1
−−
=−−
=β
β1= 1.1
06.383.318.141.1
1152log6792log15log26log2
−−
=−−
=β
β2= 0.3
βλtN
=
1.1792141 =λ
λ1=0.009
3.06792262 =λ
λ2=1.84
The estimated instantaneous failure rates at 792 and 6792 days
are: 11.1
792 7921.1009.0)( −= xxtw
= 0.019 f/day
MTBF=1/ w(t)x24
=1263 hrs 13.0
6792 679230.084.1)( −= xxtw
= 0.001 f/day
MTBF=1/ w(t)x24
MTBF=24000 hrs
)240006792(
)( 16792
−−= eF t
247.0)( 6792=tF
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R(t)=1-0.24=0.753
4.1.2.5.2 Estimating the MTTR Re-arranging the repair data and truncating them
Table 11: Boiler repair data
Plotting the data using the exponential distribution model
From the plot, µ is 1.89654
MTTR=1/µ
=1/1.89654
MTTR=0.52 hrs
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Figure 25: Boiler repair model graph
52.02400024000)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.9999
4.1.2.6 Pre-fill and exhaust valve no.6 4.1.2.6.1 Estimating failure model MTBF
Re-arranging the data and truncate them
Table 12: valve no.6 TTF and TTR
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Plotting the failure data using, Weibull distribution and estimating
the failure model parameters.
Figure 26: Valve no.6 Weibull distribution graph
For Weibull distribution, the
MTBF=η[(1+Β)/Β]
Where,η= 16735.2 hrs
Β = 0.88
MTBF=16735.2[(1+0.88)/0.88]
MTBF=35752.47 hrs
47.357528760
1)8760(−
−= eF
F(8760)=0.217
R(t)=1-0.217=0.782
4.1.2.6.2 Estimating repair model MTTR Plotting the repair data using the exponential distribution
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Figure 27: V/v no.6 exponential distribution repair graph
MTTR= 1/µ, where µ=2.23333
= 1/2.23333 MTTR= 0.44 hr
44.047.3575247.35752)(+
=+
=MTTRMTBF
MTBFAtyAvailabili
A=0.999
4.1.2.7 Blocking magnet 4.1.2.7.1 Estimating failure model parameters
Since the solenoid is replaced every time it fails, therefore the
failure data can be considered as complete data.
Table 13: Blocking magnet TTF
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Figure 28: Blocking magnet failure graph
From the distribution we obtain,
η = 11430 hrs
β =0.49
4.17 51.0
1143008760
1143049.0)8760(
−
⎟⎠⎞
⎜⎝⎛ −
=λ
5108.4 −= xλ
MTTF=1/ λ
=20788.5hrs tetR λ−=)(
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)8760108.4( 5
)8760( xxeR−−=
656.0)( =tR
656.01)(1)( −=−= tRtF
344.0)( =tF
)()( tFtQ =
1)()( =+ tQtA
A=1-0.344
A=0.656
4.1.2.8 High-pressure pump no. 5
The failure parameters are supplied by the manufacturer-
Hammellmann GMBH – Germany. The manufacturer indicated that
this pump exhibits constant failure rate.
MTBF= 25000 hrs
3504.025000
11===
MTBFλ 4.18
)(1)( MTBF
t
etF−
−= 4.19
)250008760(
1)8760(−
−= eF
296.0704.01)8760( =−=F
704.0)8760( =R
5.22500025000)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
MTTR= 2.5 hrs
A=0.999
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4.1.2.9 Non-return valves There are no data exist for these valves, therefore we will make
use of the generic data available from the reliability data handbook
[72]. 61056.4 −= xλ
MTBF=1/λ 4.20
=219298 hrs
Assuming a constant failure rates from similar equipment
distribution
]8760).1056.4[( 06
)8760(−−= xeR
961.0)8760( =R
039.0961.01)8760( =−=F
)()( tFtQ =
1)()( =+ tQtA
A=1-0.039
A=0.961
4.1.2.10 Thruster valves 1- 4 4.1.2.10.1 Estimating the MTBF
Re-arranging the data from the lost time database and truncate
them
Table 14: Thruster valves TBFs
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Since the valves are repairable, we check them against HPP
Table 15: Thruster valves TBFs against N(T)
We plot the data to verify the model
Thruster valves
02468
10121416
0 500 1000 1500 2000
TBF
N(T
)
Figure 29: Thruster valves TBF against N(T)
Since its curve, we will test it against NHPP, by taking the log of
the values and plotting them
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Table 16: Thruster valves log TBF against log N(T)
Figure 30: Thruster valves log TBF-log N(T)
As the plot is straight line, hence it’s a NHPP
Using DA-AMSSA model, we determine the model parameters 1)( −= βλβ ttw
Where
4.117.209.0
52log149log1log8log1
−−
=−−
=β
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β1= 1.16
6.225.395.015.1
402log1764log9log14log2
−−
=−−
=β
β2= 0.3
βλtN
=
16.114981 =λ
λ1=0.024
3.01764142 =λ
λ2=1.48 The estimated instantaneous failure rates at 149 and 1764 days
are:
dayfxxtw /006.01496.1024.0)( 116.1149 == −
= 0.06 f/day
MTBF=1/ w(t)x24
=400 hrs
dayfxxtw /0023.017643.048.1)( 13.01764 == −
MTBF=1/ w(t)x24=10435 hrs,
The instantaneous failure rates refers to the population of 4
thrusters, therefore at 1764 days, the MTBF is
MTBF =10435x4=41739hrs
)417398760(
)( 1)8760(1764
−−= eF t
189.0)8760()( 1764=tF
R(t)=1-0.189=0.810
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4.1.2.10.2 Estimating the MTTR Re-arranging the repair times data
Table 17: Thruster valves repair data
Plotting the data using the log- normal distribution
Figure 31: Thruster valves log-normal repair model plot
From figure 33, µ = 0.472 and σ = 0.53
2
21σµ+
= eMTTR
MTTR= 1.8 hrs
8.14173941739)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.9998
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4.1.2.11 Tubular guides 4.1.2.11.1 Estimating MTBF
Re-arranging the data and verify the model against HPP
Table 18: Tubular guides’ failure data
Table 19: Tubular guides TBF-N(T)
Plotting the data against N(T)
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Tubular guides
0
5
10
15
20
25
30
35
0 200 400 600 800 1000 1200
TBF
N(T
)
Figure 32: Tubular guides TBF-N(T)
Since it is not a straight line, hence its not a HPP.
We take the log of the data and plot them to check against NHPP
Table 20: Tubular guides Log TBF-Log N(T)
Plotting the data to estimate the process and its parameters
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Figure 33: Tubular guides log TBF-log N(T)
1)( −= βλβttw
78.077.1008.1
6log59log1log12log1
−−
=−−
=β
β1= 1.1
77.118.211.132.1
59log151log13log21log2
−−
=−−
=β
β2= 0.512
18.201.334.149.1
152log1017log22log31log3
−−
=−−
=β
β3= 0.08
βλtN
=
1.159121 =λ
λ1=0.13
51.0151212 =λ
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λ2=1.62
08.01017313 =λ
λ2=17.8
The estimated instantaneous failure rates at 59, 151 and 1017
days are:
dayfxxtw /21.0591.131.0)( 11.159 == −
MTBF=1/ w(t)x24
= 114.2 hrs
dayfxxtw /07.0151512.062.1)( 1512.0151 == −
= 0.07 f/day
MTBF=1/ w(t)x24
= 342 hrs
dayfxxtw /02.0101708.08.17)( 108.01017 == −
MTBF=1/ w(t)x24
MTBF=9850 hrs
)98501017(
)( 11017
−−= eF t
099.0)( 121=tF
901.0)( 121=tR
4.1.2.11.2 Estimating MTTR We arrange the data and truncate them, and then we plot them
using the log- normal distribution plot.
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Table 21: Tubular guides repair data
Figure 34: tubular guides Log-normal plot of repair data
From the graph, µ=0.21, and σ=0.2
2
21σµ+
= eMTTR
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MTTR=1.25hrs
25.198509850)(+
=+
=MTTRMTBF
MTBFAtyAvailabili
A=0.9998
4.1.2.12 Heating platens leaks 4.1.2.12.1 Estimating the MTBF
Taking failure data for 3 year (26280 hrs) as shown in table 22
Table 22: Heating platens failure data
failuresofno
timespairfailureXreofnoplatensofhrsXnoMTBF..
)...()..26280( −= 4.21
18)25.018()3026280( XXMTBF −
=
MTBF=43799hrs
)(1)( MTBF
T
eTF−
−=
)437998760(
1)8760(−
−= eF
F (8760) =0.182
R (8760) =0.828
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4.1.2.12.2 Estimating the MTTR Re-arranging the data and plotting them using the exponential
distribution
Table 23: Heating platens repair data
Figure 35: Heating platen exponential repair model
MTTR=1/µ
From the repair time model graph;
µ=3.218
MTTR=0.31hrs
31.04379943799)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
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4.1.2.13 Link pipes 4.1.2.13.1 Estimating the MTBF
Taking failure data for 1 year (8760 hrs) as shown in table 24
Table 24: Link pipes failure data
Since the pipes are repairable, hence;
failuresofnotimespairfailureXreofnopipesofhrsXnoMTBF
..)...()..8760( −
=
32)25.032()608760( XXMTBF −
=
MTBF=131400hrs
)(1)( MTBF
T
eTF−
−=
)131400
8760(1)8760(
−−= eF
F (8760) =0.065
R (8760) =0.935
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4.1.2.13.2 Estimating the MTTR
Table 25: Link pipes ranked repair times
Plotting the repair times using log-normal distribution
From the graph, µ=0.244, and σ=0.32
2
21σµ+
= eMTTR
MTTR=0.74hrs
74.0131400131400)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
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Figure 36: Link pipes repair graph
4.1.2.14 Bleeding valve no.7 4.1.2.14.1 Estimating the MTTF
From the generic data handbook
Median rank=11 f/mh 5101.1 −= xλ
MTTF=1/λ
MTTF=90909hrs tetR λ−=)(
8760101.1 5
)8760( XxeR−−=
R(8760)=0.908
1)()( =+ tFtR
F(8760)=1-0.908
F(8760)=0.092
)()( tFtQ =
1)()( =+ tQtA
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A=1-0.092
A=0.908
4.1.2.15 Temperature sensor From the generic data handbook
Median rank=6.8 f/mh
=0.0595f/yr 6108.6 −= xλ
MTTF=1/λ
=147058hrs tetR λ−=)(
8760108.6 6
)8760( XxeR−−=
R(8760)=0.942
1)()( =+ tFtR
F(8760)=1-0.94
F(8760)=0.058
)()( tFtQ =
1)()( =+ tQtA
A=1-0.06
A=0.942
4.1.2.16 Exhaust fan bearings
From the generic data handbook
Median rank=11.4 f/mh
=0.998 f/yr
λ=1.14x10-5
MTTF=87719.29hrs tetR λ−=)(
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87601014.1 5
)8760( XxeR−−=
R(8760)=0.904
1)()( =+ tFtR
F(8760)=1-0.904
F(8760)=0.096
)()( tFtQ =
1)()( =+ tQtA
A=1-0.096
A=0.904
4.1.2.17 Pressure sensor
From the generic data handbook
Median rank=28 f/mh 5108.2 −= xλ
MTTF=35714.28hr tetR λ−=)(
8760108.2 5
)8760( XxeR−−=
R(8760)=0.782
1)()( =+ tFtR
F(8760)=1-0.782
F(8760)=0.218
)()( tFtQ =
1)()( =+ tQtA
A=1-0.22
A=0.782
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4.1.2.18 Timer From the generic data handbook
6102.1 −= xλ
MTTF=833333hrs tetR λ−=)(
8760102.1 6
)8760( XxeR−−=
R(8760)=0.989
1)()( =+ tFtR
F(8760)=1-0.989
F(8760)=0.011
)()( tFtQ =
1)()( =+ tQtA
A=1-0.01
A=0.989
4.1.2.19 Hydrauphore tank 4.1.2.19.1 Estimating the MTBF
From the generic failure data, we will model the failure rate using
the Design, Operating, Environment (DOE) method. In 1996 Andres
and Moss [192] proposed this model for calculating stress factors for
rotating machinery. This basic model has now been modified for use
with process equipment. The basis of the model is the adoption of a
reference point (generally the median of the range of recorded generic
failure rates for similar equipment.) to define a reference equipment
and base failure rate [72]
∏= iAbXA Kαλλ 4.22
Where;
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XAλ = Predicted failure rate for equipment X.
bλ = Best estimate of a mean failure rate for similar equipment to
X
aα =Proportion of failures (=1, if all failure modes are included)
∏ iK =Product of various "k" factors, xiK 2=
Assumptions:
* All failure modes are considered (i.e. αa=1)
* The equipment is operating in a typical industrial
environment (non corrosive onshore plant).
* A log normal relationship between equipment failure
rates and operating conditions.
Figure 37: Log-normal distribution of centrifugal pump and circuit breaker failure rates
(independent samples)(T.R.Moss) [72]
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Fig 37 shows the log-normal distribution of observed centrifugal
pump & circuit breaker failure rates, the evidence gives credence to
the adoption of the simple failure rate prediction model used, where
the generic failure rates range is scaled logarithmically with the general
format.
Method: First we obtain generic data from different sources to get the best
estimate.
Table 26: Generic data from different sources
Taking geometric mean to obtain the range best estimate failure
rate
Hence, the ordered failure rates estimate is:
Table 27: Ordered failure rates data estimates
Based on the median rank, the Best estimate λ = 16.5 f/mh
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We obtain the K values from stress ranking table
Design attributes: Table 28: Hydrauphore tank design attributes
Mean design weight=(0+(-1.5)+0+(-1)/4=-0.625
k1=2
=1.54
Operating attribute: Table 29: Operating attributes
Weight difference = -1/4 = -0.25 )25.0(22 −=K
=0.84
Environment attributes:
Table 30: Environment attributes
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)25.0(22 =K =1.19
πki=1.54 x 0.84 x 1.19=1.539
λXA=1.9 x 1.0 x 1.539=2.924 failures/mh 0610924.2 −= xλ
MTBF=1/λ
MTBF=341997h
Assuming a constant failure rate from similar equipment tetR λ−=)(
]8760).0413.0[()8760( −= eR
974.0)8760( =R
025.0974.01)8760( =−=F
4.1.2.19.2 Estimating the MTTR
Based on information provided by engineering staff
MTTR= 3 hrs
33419934199)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
4.1.2.20 Valve no.11 4.1.2.20.1 Estimating the MTBF
Shift fitter fail to open valve after shutdown (human error)
From generic failure rate tables for single operation task
Median rank=5.5 f/mh
λ=5.50E-06
MTBF=1.82E+05 tetR λ−=)(
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]8760).105.5[( 06
)8760(−−= xeR
952.0)8760( =R
048.0952.01)8760( =−=F
Since this is unrevealed failure,
Hence, the probability of being in the failed state in 2 weeks time
(say 336 hrs) is the Fractional Dead Time FDT:
T: inspection interval=336 FDT=λΤ/2 4.23
9.24E-04
Unavailability (U) =FDT
U+A=1 4.24
A=1-9.24E-04
A=0.999
4.1.2.20.2 Estimating the MTTR
Based on information provided by engineering staff,
MTTR= 2.0 hrs
4.1.2.21 Air compressor 4.1.2.21.1 Estimating the MTBF
From the generic failure data, we model the failure rate using
DOE method
∏= iAbXA Kαλλ Where;
λXA= Predicted failure rate for equipment X.
λb = Best estimate of a mean failure rate for similar equipment to
X
αa = Proportion of failures (=1, if all failure modes are included)
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πki = Product of various "k" factors, Ki=2х
* All failure modes are considered (i.e. αa=1)
* Typical reciprocating air compressor operating in
industrial environment
Design attributes: Table 31: Design attributes
)5.2(21 −=K 0.18
Operating attribute: Weight difference=-1
)5.0(22 =K =0.5
Environment attributes: Weight difference=-1
)5.0(22 =K
=0.5
πki=0.18 x 0.5 x 0.5=0.045
λXA=4 x 1.0 x 0.045=0.18 failures/mh 07108.1 −= xλ
07108.111
−==x
MTBFλ
]8760).108.1[( 07
)8760(−−= xeR
998.0)8760( =R
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002.0998.01)8760( =−=F
MTBF=5555555.5h
4.1.2.21.2 Estimating the MTTR
Based on information provided by engineering staff,
MTTR= 0.5 hrs
5.05.55555555.5555555)(+
=+
=MTTRMTBF
MTBFAtyAvailabili
A=0.999
4.1.2.22 Control valves no.8 & 9
From the generic failure data, we model the failure rate using
DOE method
∏= iAbXA Kαλλ Where;
λXA=Predicted failure rate for equipment X.
λb= Best estimate of a mean failure rate for similar equipment to
X
αa=Proportion of failures (=1, if all failure modes are included)
πki=Product of various "k" factors, Ki=2х
Assumptions: * All failure modes are considered (i.e. αa=1)
* The control valves operating in a typical industrial
environment
* a log relationship between equipment failure rates and
operating conditions
Method: First we obtain generic data from different sources to get the best
estimate
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Table 32: Generic data from different sources
Taking geometric mean to obtain the range best estimate failure
rate
√ 0.4 x80=5.656 f/mh
√ 4.56 x 79.9=19.087 f/mh
Hence, the ordered failure rates estimate is
Table 33: Ordered failure rates estimates
Based on the median rank, the Best estimate λ = 19.087 F/mh
We obtain the K values from stress ranking table
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Design attributes: Table 34: Design attributes
Operating attribute:
Table 35: Operating attributes
)0(22 =K
=1.00
Environment attributes:
Table 36: Environment attributes
Weight difference= (1+0+0+1)/4
)5.0(22 =K
= 1.414
πki=1 x 1 x 1.414=1.414
λXA=19.087 x 1.0 x 1.414=27.0 failures/mh
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0506 107.21027 −− == xxλ
]8760).1027[( 05
)8760(−−= xeR
789.0)8760( =R
211.0789.01)8760( =−=F
MTBF=1/λ
MTBF=370374h
MTTR=1 hrs
13703737037)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
4.1.2.23 Change over valve no.5
From tables=4.7 f/mh
λ=0.0000047
MTBF=1/ λ=1/0.0000047
MTBF=212766
2127668760
1)8760(−
−= eF
041.0)8760( =F
959.004.01)8760( =−=R
MTBF=12516h
MTTR=3hrs
3212766212766)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
4.1.2.24 PLC From tables=29.7 f/mh
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λ=2.97X10-5
]8760).1097.2[( 05
)8760(−−= xeR
77.0)8760( =R
23.0771.01)8760( =−=F
MTBF=33670h
MTTR=1.5hrs
5.13367033670)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
4.1.2.25 Piping system From generic tables
Median failure rate=1.24e-4 f/mh
λ=2.19x10-8
]8760).1019.2[( 08
)8760(−−= xeR
9998.0)8760( =R
0002.0998.01)8760( =−=F
MTBF=45662100h
MTTR=1.0hr
0.14566245662)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
4.1.2.26 Platens off position 4.1.2.26.1 Estimating the MTBF
Taking failure data for 1 year (8760 hrs) as shown in table 37
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Table 37: Platens’ failure data
Since the platens are repairable, hence;
failuresofnotimespairfailureXreofnoplatensofhrsXnoMTBF
..)...()..8760( −
=
18)37.118()308760( XXMTBF −
=
MTBF=14598hrs
)(1)( MTBF
T
eTF−
−=
)145988760(
1)8760(−
−= eF
F (8760) =0.452
R (8760) =0.548
4.1.2.26.2 Estimating the MTTR Censoring the repair data, rank them and plot them on
exponential distribution
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Table 38: Platens’ off-position repair data
nt
MTTR iΣ= 4.25
hrsMTTR 37.118
8.24==
37.11459814598)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
4.1.2.27 Hydrauphore relief valve From generic data tables
05103.2 −= xλ
]8760).103.2[( 05
)8760(−−= xeR
817.0)8760( =R
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183.0817.01)8760( =−=F
MTTF=43478.2 hrs
MTTR=1hrs
0.12.434782.43478)(+
=+
=MTTRMTBF
MTBFAtyAvailabili
A=0.999
4.1.2.28 Hydraulic storage tank parameters
From generic data tables
Failure rate=0.15f/mh 07107.5 −= xλ
MTBF=1/λ=1754386hrs
MTTR=3hrs
]8760).107.5[( 07
)8760(−−= xeR
995.0)8760( =R
005.0995.01)8760( =−=F
317543861754386)(
+=
+=
MTTRMTBFMTBFAtyAvailabili
A=0.999
Appendix 1 shows the system components calculated
Unreliability, Reliability and Availability.
4.1.3 Fault Tree Analysis (FTA) Fault tree diagrams are logic block diagrams that display the
state of a system (top event) in terms of the states of its components
(basic events). Fault tree diagrams are a graphical design technique.
An FTD is built top-down and in term of events rather than blocks. It
uses a graphic "model" of the pathways within a system that can lead
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to a foreseeable, undesirable loss event (or a failure). The pathways
interconnect contributory events and conditions, using standard logic
symbols (AND, OR etc). The basic constructs in a fault tree diagram
are gates and events.
The main purpose of the fault tree analysis is to evaluate the
probability of the top event by using the analytical or the statistical
methods. These calculations involve system quantitative reliability and
maintainability information, such as failure probability, failure rate, or
repair rate. FTA can provide useful information concerning the
likelihood of a failure and the means by which such a failure could
occur. Efforts to improve system safety and reliability can be focused
and refined using the results of the FTA.
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4.1.3.1 Constructing the fault tree model
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4.1.3.2 Estimating the system probability of failure from FTA a) Not operating safely
The Blocking magnets system S7, S8 and S9 is operating on 2
out of 3 bases, therefore the probability of failure is: 23 3)( PFFtFs += 4.26
23/ 35.065.0335.0)( xxtF MGNTB +=
27.0)(/ =tF MGNTB
)}](1{)}(1[{1)( /// tFxtFtF MGNTBFANEXHOPSS −−−=
}]27.01{}1.01[{1/ −−−= xF opsS
343.0/ =opsSF
b) Insufficient Pressing Pressure (IPP) gate
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• High Pressure Pump assembly no. 1 gate
)}](1)}.{(1)}.{(1[{1)( / tFtFtFtF vthrsutervnrvhpphppassy −−−−=
4.27
256.0}]189.01{}039.01{}046.01[{1)( =−−−−= xxtFhppassy
744.0256.011 =−=−= hppassyhppassy FR
And;
= HPP assy no.2, HPP assy no.3, HPP assy no.4
• High Pressure Pumps (1-4) assemblies ¾ voting gate 2234
41 64)( FPPFFtFhpps ++=− 4.28
235.0
)236.0(764.06)236.0(764.04)236.0()( 223441
=
++=− xxxxtFhpps
• High Pressure Pumps (1-4) and 5 Assembly gate
069.0296.0235.0)().51( ==− xtF assyHPP .
• Pressure reducing valve 7 assembly gate
)}](1)}.{(1[{1)( 7#/4).7/( tFtFtF avvSassyavv −−−=
136.0}]092.01{}049.01[{1)().7/( =−−−= xtF assyavv
And;
= V/V no.7b assembly
=)(7#/ tF ASSYVV )}]()}.{([{1 .7/).7/( tFtF assybvvassyavv− ).
253.0}]136.01{}136.01[{1)(7#/ =−−−= xtF ASSYVV
)}](1.{)}(1)}.{(1)}.{(1.{
)}(1)}.{(1)}.{(1[{1)(
)51(
7#/.//
.6#/
tFtFtFtF
tFtFtFtF
assyHPP
ASSYVVNRVLISNSRP
syspipingtimervvIPP
−−−−−
−−−−=
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}]069.01{}253.01{}038.01{}218.01{}0002.01{}011.01{}217.01[{1)(
−−−−−−−−=
xxxxxxtFIPP
594.0)8760( =IPPF
c) Low Press Temperature gate
)}](1)}.{(1)}.{([{1)( // tFtFtFtF snsrtpipeslboilerLPT −−−=
336.0}]058.01{}065.01{}247.01[{1)8760( =−−−−= xxFLPT
d) Delaminated Product surface gate
)}](1)}.{(1[{1)( ..min tFtFtF LPTleakpltnsHationDela −−−=
456.0}]336.01{}182.01[{1)8760(min =−−−= xF ationDela
e) Product sticking on surface plate gate
)}](1)}.{(1[{1)( . tFtFtF LPTQualityPulpSticking −−−=
336.0}]336.01{)}(1[{1)8760( . =−−−= xtFF QualityPulpSticking
f) Wet Lap Misalignment gate
}]099.01[{1)}](1)}.{(1)}.{(1[{1)( / −−=−−−−= tFtFtFtF crctPpusherTGntMisalignme
099.0)8760( =ntMisalignmeF
• Hydrauphore system failure gate
))](1)).((1(
)).(1)).((1[(1)(
tan.2#/
1#//.
tFtF
tFtFtF
kHdrphrvvR
vvRCompAsysHdrphr
−−
−−−=
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350.0}]025.01{}183.01{
}183.01{}002.01[{1)8760(.
=−−
−−−=
x
xxF sysHdrphr
• Low Pressure Pumps assembly failure gate
))().()( 2#1# tFtFtF lpplppLPPs =
013.0117.0117.0)8760( == xFLPPs
• Valve No. 5 assembly failure gate
)}](1)}.{(1[{1)( 5$/1.5#/ tFtFtF vvSassyVV −−−=
087.0}]041.01{}049.01[{1)8760(.5#/ =−−−= xF assyVV
g) Press Not Pressing Pulp Mats gate
)}](1)}.{(1)}.{(1)}.{(1.{)}.(1)}.{(1)}.{(1[{1)(
..5#/
tan.11#/6#/.
tFtFtFtFtFtFtFtF
sysHdrphrLPPsassyVVNRV
kstvvvvmatsPLP
−−−−−−−−=
}]350.01{}013.01{)087.01{}039.01{}005.01{}048.01{}217.01[{1)8760(.
−−−−−−−−=
xxxxxxF matsPLP
574.0)8760(. =matsPLPF
h) Overall system probability of failure (Top main gate)
)}](1)}.{(1)}.{(1)}.{(1{
)}.(1)}.{(1)}.{(1[{1)(
.min
/
tFtFtFtF
tFtFtFtF
matsPLPentMisallignmStickingationDela
LPTIPPopsSPRESS
−−−−
−−−−=
}574.01{}099.01{}336.01{}456.01{}0336.01{}594.01{343.01[{1)8760(
−−−−−−−−=
xxxxxxFPRESS
97.0)8760( =PRESSF
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4.1.4 Reliability Block Diagram Analysis
Figure 38: Pressing system RBD
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4.1.4.1 Estimating the system reliability To analyze this complex system, we will break it down to sub-
assemblies, and calculate the reliability of each assembly separately,
and then combined them back together as group assemblies.
• High pressure pumps HPPs
Figure 39: High pressure pumps 1-4 assembly
All the pumps are identical, so are their thrusters and non-return
valves.
1#1#/.11# .. nrvvvThrustehppassyhpp RRRR = 4.29
74.0961.0810.0953.01# == xxR assyhpp
26.074.011# =−=assyhppF
Since the HPPs are identical and independent. Hence the
reliability of them are also identical.
Therefore;
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assyhppassyhppassyhppassyhpp RRRR 4#3#2#1# ===
The assembly is an identical and independent parallel
(K-Out of-n).
Therefore the reliability of such system is:
FPPRHPP34
)41( 4+=− 4.30
72.026.074.0474.0 34)41( =+=− xxRHPP
HPPs 1-4 are in parallel with HPP no.5, therefore;
( )∏=
−−=2
1
11i
HPPs RiR 4.31
( ) )]704.01).(72.01[(1112
1
−−−=−−= ∏=i
HPPs RiR
917.0=HPPsR
• Low pressure pumps sub-assembly The 2 low-pressure pumps are in parallel
Figure 40: Low pressure pumps
( )∏=
−−=2
1
11i
LPPs RiR
)]883.01).(883.01[(1 −−−=LPPsR
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986.0=LPPsR
And they are in series with solenoid S1, valve no.8 and change
over valve no.5.
Figure 41: Low pressure pumps assembly
329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS RRRRRRRR =
951.0951.0789.0789.0959.0951.0986.0 xxxxxxRLPPsSYS =
506.0=LPPsSYSR
• Hydrauphore sub-assembly
Figure 42: Hydrauphore sub assembly
( )∏=
−−=2
111#//tan/tan.. ].11.[.
ivvvireliefevkHydrphoreCompAkStassyHydrphr RRRRRR
)]817.01)(817.01[(1952.0974.0998.0995.0. −−−= xxxxR assyHydrphr
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888.0. =assyHydrphrR
• Heating system
Figure 43: Heating system
snsrTleakPpipesLboilersysH RRRRR ///. ...=
542.0942.0818.0935.0753.0. == xxxR sysH
Remaining sub-assembly
Figure 44: Remaining sub assembly
syspipngsnsrptimerPLC
assybvvassyavvnrvvvRassy
RRRR
RRRRR
..
.7..7.6#/
....
)}].1)(1{(1)}].[1)(1{(1[ −−−−−−=
47.7.7 Savvassybvvassyavv xRRRR ==
863.0951.0908.0.7.7 === xRR assybvvassyavv
981.0)]863.01)(863.01[(17# =−−−=VVR
991.0)]961.01)(783.01[(1.6# =−−−=AssyVVR
58.0999.0782.0989.077.0981.0991.0. == xxxxxxR assyR
FPPR AssyMgntBlkng23
.. 3+=
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726.0344.0656.03656.0 23.. =+= xxR AssyMgntBlkng
Figure 45: Press assembly
)}]1)(1)(1)(1{(1.[ ./... fanExTGspoPRamsassymgntBlkngpress RRRRRR −−−−−=
71.0)}]904.01)(901.01)(548.01)(48.01{(1[726.0 =−−−−−= xRpress
Now that the complex configuration has been reduced to a simple
assembly, we can estimate the whole system reliability.
Figure 46: Pressing plant simplified RBD
essRassysysHeatingAssyLPPsAssyHPPssystemeHydrauphorplantP xRxRxRxRxRRR Pr..... =
71.058.0542.0506.0917.0888.0. xxxxxR plantP =
16.0. =plantPR
4.1.5 Estimating the system Availability To analyze this complex system, we will break it down to sub-
assemblies, and calculate the Availability of each assembly separately,
and then combined them back together as group assemblies.
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• High pressure pumps HPPs All the pumps are identical, so are their thrusters and non-return
valves.
1#1#/.11# .. nrvvvThrustehppassyhpp AAAA =
959.0961.0999.0999.01# == xxA assyhpp
Since the HPPs are identical and independent, hence the
availability of them is also identical.
Therefore;
assyhppassyhppassyhppassyhpp AAAA 4#3#2#1# ===
The assembly is an identical and independent parallel
(K-Out of-n).
Therefore the Availability of such system is:
989.0041.0
959.04959.04 3434)41(
=
+=+=−
x
xUAAAHPP 4.32
HPPs 1-4 are in parallel with HPP no.5, therefore;
( )∏=
−−=2
1
11i
HPPs AiA
( ) )]999.01).(989.01[(1112
1
−−−=−−= ∏=i
HPPs AiA
99.0=HPPsR
• Low pressure pumps sub-assembly The 2 low-pressure pumps are in parallel
( )∏=
−−=2
1
11i
LPPs AiA
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)]999.01).(999.01[(1 −−−=LPPsA
999.0=LPPsA
And they are in series with solenoid S1, valve no.8 and change
over valve no.5.
329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS AAAAAAAA =
951.0951.0999.0999.0999.0951.0999.0 xxxxxxALPPsSYS =
85.0=LPPsSYSA
• Hydrauphore sub-assembly
( )∏=
−−=2
111#//tan/tan.. ].11.[.
ivvvireliefevkHydrphoreCompAkStassyHydrphr AAAAAA
)]999.01)(999.01[(1999.0999.0998.0999.0. −−−= xxxxA assyHydrphr
99.0. =assyHydrphrA
• Heating system
leakPlatensnsrTpipesLboilersysH AAAAA .//. ...=
99.0999.0942.0999.0999.0. == xxxA sysH
• Remaining sub-assembly
syspipngsnsrptimerPLC
assybvvassyavvnrvvvRassy
AAAA
AAAAA
..
.7..7.6#/
....
)}]1)(1{(1)}].[1)(1{(1[ −−−−−−=
47.7.7 Savvassybvvassyavv xAAAA ==
950.0951.0999.0.7.7 === xAA assybvvassyavv
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997.0)]950.01)(950.01[(17# =−−−=VVA
999.0)]961.01)(999.01[(1.6# =−−−=AssyVVA
UAAA AssyMgntBlkng23
.. 3+= 4.33
726.0344.0656.03656.0 23.. =+= xxA AssyMgntBlkng
768.0782.0989.0999.0999.0997.0999.0. == xxxxxxA assyR
)}]1)(1(
)1)(1{(1.[
.
/...
fanExTGs
poPRamsAssyMgntBlkngpress
AA
AAAA
−−
−−−=
726.0
)}]904.01)(999.01)(999.01)(997.01{(1[726.0
=
−−−−−= xApress
Now that the complex configuration has been reduced to a simple
assembly, we can estimate the whole system Availability.
essRassysysHeating
AssyLPPsAssyHPPssystemeHydrauphorplantP
xAxAA
xxAxAAA
Pr.
.... =
726.076.0993.0857.0999.0995.0. xxxxxA plantP =
47.0. =plantPA
4.2 Monte Carlo Simulation
Monte Carlo simulation is a method for iteratively evaluating a
deterministic model using sets of random numbers as inputs. This
method is often used when the model is complex, nonlinear, or
involves more than just a couple uncertain parameters. A simulation
can typically involve thousands evaluations of the model, a task which
in the past was only practical using super computers [193]. With this
method, a large and complex system can be sampled in a number of
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random configurations, and that data can be used to describe the
system as a whole.
To be able to handle the complexities and strong dependencies
of the system, we will use a computer program which employs Monte
Carlo simulation methods to estimate system and sub-system
parameters, such as unavailability, expected number of failures, costs
etc.
Also simulation can handle the reliability behavior of repairable
components with non-constant failure rates.
The process involves synthesizing system performance over a
given number of simulation runs [194]. Each simulation run emulates
how the system might perform in real life based on the input data
provided to the fault tree or the reliability block diagram RBD, which in
turn will inform the program how the components failures interact to
cause the system failures, while the failure and maintenance
parameters will inform the program on how often the components are
likely to fail and how quickly they can be restored to service.
Performing many simulation runs enables the program to build a
statistical picture of the system performance by emulating the chance
variations. This is done by using the Microsoft run time library to
generate pseudo random numbers.
Figure 47 shows the simulation sequence that we will use.
In addition to the Failure Mode, Effects and Criticality Analysis
FMECA to further assess the reliability of the system, we will use Fault
Tree Analysis FTA and Reliability Block Diagram RBD, but before we
construct the Fault Tree Analysis FTA and the reliability Block Diagram
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RBD models and simulate them, we need to estimate the parameters
of the system components, i.e. MTBF, MTTR. This will be one by
means of fitting the history failure data that we gathered from the plant
performance and lost time database into distribution models and
estimate the required parameters.
Figure 47: Illustrates the used simulation sequence
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4.3 Evaluating the system performance To evaluate the system performance and predict its future
behavior based on its history, we accelerate its time performance by
simulating the existing system with the current maintenance policies to
obtain its data. This is done by keying in the program the current
maintenance policy for each component, its frequency, used spare part
and its cost, number of technicians and their hourly rates
The process will involve synthesizing the system performance
over a given number of simulation runs using a computer program.
Each simulation run will in effect emulates how the system might
perform in real life based on the input data that we provide. The input
data can be divided into two categories- a failure logic diagram and
quantitative failure and maintenance parameters. The logic diagram
(FT & RBD) informs the computer program how component failures
interact to cause system failures. The failure and maintenance
parameters inform the program how often components are likely to fail
and how quickly they will be restored to service. By performing many
simulation runs the computer program can build up a statistical picture
of the system performance by recording the results of each run.
The Monte Carlo simulation must emulate the chance variations,
which will affect system performance in real life, to do this the
computer must generate random numbers, which form a uniform
distribution, and since the rules used on the computer to generate the
number sequences are deterministic then the numbers will be pseudo-
random producing a uniform distribution [194].
The cumulative failure distribution at time t for a three-parameter
Weibull distribution is;
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β
ηγ⎟⎟⎠
⎞⎜⎜⎝
⎛ −−
−=t
etF 1)(
By setting 0=γ and taking the log of both sides of this equation
we obtain
))(1ln( tFt−−=⎟⎟
⎠
⎞⎜⎜⎝
⎛β
η 4.34
Re-arranging the above equation
))(1ln( tFt −−=η 4.35
With this equation, we can sample times to failure TTF for the
component by first generating a set of uniform random numbers x1, x2,
x3, x4, etc. (between 0 and 1 ) and then substituting these values as
F(t) into the equation above
Table 39: Illustrates obtaining TTF values by simulation
By following the same procedure for the repair models of the
system, we obtain the following expression:
))(1ln(. tGMTTRt −−= 4.36
Where, G(t) is the cumulative repair distribution.
Based on the number of simulations, the following estimates can
be made:
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Mean Unavailability = total downtime/total simulation time 4.37
Unreliability at (t) = number of times system failed at
least once during a simulation / no. of simulations 4.38
We re-arrange the failure modes, to analyze the system
maintenance policies and their effect.
Appendix 2 shows the system hierarchy
To determine how effective (beneficial) each maintenance task is,
we will calculate the criticality values, which represents the severity of
the effects associated with the cause combined with its frequency of
occurrence from the following equations for each failure mode:
1) Cost Benefit Ratio (CBR).
alarmsandtasksspecifiedwithoutCostalarmsandtasksspecifiedwithCostCBR.....
.....= 4.39
A cost benefit ratio less than 1 indicates that the
task/alarm is worthwhile from a cost point of view.
2) Safety/Environmental Ratio (SBR).
alarmsandtasksspecifiedwithoutycriticalitSafetyalarmsandtasksspecifiedwithycriticalitSafetySBR......
......= 4.40
A safety/environmental benefit ratio less than 1
indicate that the task/alarm is worthwhile from a
safety/environmental point of view.
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3) Operational Benefit Ratio (OBR).
alarmsandtasksspecifiedwithoutycriticalitlOperationaalarmsandtasksspecifiedwithycriticalitlOperationaOBR......
......= 4.41
An operational benefit ratio less than 1 indicate that
the task/alarm is worthwhile from a operational point of
view.
To estimate the system parameters, we assign the following:
• The estimated failure and repair parameters.
• The maintenance policies associated with the component
or equipment imported from CMMS.
• The different costs associated with these maintenance
policies, i.e. type of spare parts and its cost, number of
labor performing the maintenance task, the duration of
the task and the labor rate
After assigning all the parameters to the model, we will simulate
the process and estimate the system availability and different cost
associated with it.
Appendix 1 shows the labor data used.
Appendix 2 shows the spare parts data used.
Appendix 3 shows the different maintenance policies of the
equipment as extracted from the plant CMMS.
Appendix 4 shows the system effects and their respective CBR,
SBR and OBR before optimization.
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Calculating the system effects data by simulation, set at 8760 hrs,
we obtain the following results
From the simulation analysis we obtain the system unavailability,
unreliability and mean failure frequency.
Where, Unavailability (U) =1- Availability (A)
MTTRMTBFMTBFA +=
R(t)=1-F(t), where t, is the system lifetime and is equal to 8760
hrs. t
etFλ−
−= 1)( For exponentially distributed components, and
tetR λ−=)( , where λ is the failure rate=1/MTTF
For Weibull distribution; β
η ⎟⎟⎠
⎞⎜⎜⎝
⎛−
−=t
etF 1)(
Mean Failure Frequency between or Average Failure Frequency
between (0,T) is
TTRTAFR )(ln),0( −
= 4.42
For repairable system;
N(t) is the counting function that keeps track of the cumulative
number of failures a given system has had from time zero to time t,
and its a step function that jumps up one every time a failure occurs
and stays at the new level until the next failure.
M(t) = the expected number (average number) of cumulative
failures by time t for these systems.
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m(t) is The derivative of M(t),, is defined to be the Repair Rate or
the Rate Of Occurrence Of Failures at Time t or ROCOF.
For Homogeneous Poisson Process (HPP)
M(t) =λt 4.43
m(t)=λ, the repair rate ROCOF t
etFλ−
−=1)(
For Non -Homogeneous Poisson Process (HPP)
])[(1)(ββη TtT
T etF −+−−= 4.44
T is the time of the just occurred failure
β is the shape parameter, and
η is the characteristic life.
Table 40, shows the system mean failure frequency, unreliability
and mean unavailability (system profile) through the system lifetime,
before optimization, which is set at 8760 hrs.
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Table 40: System profile before optimizing
Table 41 shows the system effects data before optimization
through the system lifetime, which is set at 8760 hrs.
Figure 48 shows the unavailability graph of the plant sub-systems
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Figure 48: Sub-systems unavailability profiles before optimization
Table 41: System effects data before optimization
Appendix 5 shows the mean unavailability over lifetime, number
of expected failures, total downtime, unavailability and unreliability at
lifetime (8760hrs) of the system components before optimization.
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Figure 49: System cost profile before optimization
From the simulation we obtain the following results:
Labor costs: $96833.1
Spare usage costs: $7383.0
Miscellaneous costs: $2503.21
Effects costs: $3157270 Total costs: $3263989.31
4.4 Optimizing the system
To optimize the system, we have to first assign different
maintenance policies to the existing ones. After assign these policies,
we will evaluate them by using CBR, SBR, and OBR. The policies that
meet the previously mentioned criteria, which are a ratio of less than 1,
will be used, and then we will re-evaluate the system and estimate the
different cost associated with the system.
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As the analysis is going to be performed by using computer
program, it’s useful to state the different models and mathematical
equation that will be used in the optimization simulation process.
4.4.1 Mathematical models and equations 4.4.1.1 Modeling the optimal replacement times for equipment which it’s
operating cost increases with usage
Total cost in interval=cost of operating + cost of replacement
and=
∫ +rt
rcdttc0
)( 4.45
4.46 Where,
c(t) is the operating cost per unit time at time t after replacement.
Cr is the total cost of a replacement.
C(tr) is the total cost per unit time for the interval(0,tr).
Equation 2, is a model of the problem relating replacement
interval tr to total cost per unit time C(tr).
The optimal replacement interval tr is that the value of tr that
minimizes the right-hand side of equation 2, which can be shown by
calculus to occur when
c(tr)=C(tr)
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Thus, the optimal replacement time is when current operating
cost rate is equal to the average total cost per unit time. i.e. the optimal
time to replace is when the marginal cost equals the average cost.
If the trend in operating cost is linear, c(t)=a+bt, then the optimal
replacement interval t* is
t*=√2Cr/b 4.47
If replacement time to be taken into account, such as production
losses incurred due to the duration of the replacement and needs to be
incorporated into the cost of replacement action, then the equation
becomes
4.48
Where;
Tr= is the replacement time, and
tr= is the replacement interval
Figure 50 shows the short-term deterministic optimization
Figure 50: Short term deterministic optimization
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Example:
If the trend in the operation cost of an item is of the form
]exp[)( KtBAtc −−= 4.49
Where A=$100,B=$80.and k=0.21/week
A-B≥0, is the operating cost per unit time if no deterioration
occurs.
K, is a constant describing the rate of deterioration
Cr, is the total cost of replacement, and is $100
∫ +−−==rt
rr dtt
ttC
0
]100])21.0exp[80100([1)( 4.50
Table 42: C(tr) values for different values of tr
Evaluating the above model for different values of tr, as shown in
the above table indicates that the optimal value of tr, is at 5 weeks.
4.4.1.2 Modeling optimal preventive replacement interval of an item subject to breakdown
This model relates the replacement interval tp to total cost C(tp).
4.51 Where,
C(tp) is total expected cost per unit time.
Total expected cost in interval (0,tp)=cost of preventive
replacement+ expected cost of failure replacement=
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Cp+CfH(tp) 4.52
H(tp) is the expected number of failures in interval (0,tp)
tp is the length of interval
Cp is the total cost of a preventive replacement.
Cf is the total cost of a failure replacement.
Figure 51: Optimal preventive replacement
Example: If given;
Cp=$5, Cf=$10, and failure occurs according to normal distribution
with mean=5weeks, standard deviation=1 week
p
pp t
tHtC
)(105)(
+= 4.53
Taking different values of tp, to calculate different values of C(tp), as seen in the table below
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Table 43: C(tp) values for different values of tp
From the table above, we see that the optimal replacement policy
is to perform the preventive replacement every 4 weeks.
A sample calculation of tp=2weeks
)4()3()][0(1[)]5()4()][1(1[)2( −Φ−−Φ++−Φ−−Φ+= HHH 4.54
From the standardized normal distribution table, we obtain;
0)5(,0)4( ≈−Φ≈−Φ
00135.0000135.0)4()3( =−=−Φ−−Φ
0)0( =H
00)0(1[)1( =+= XHH
00135.000135.0)01(0)01()2( =+++= xxH
Therefore,
weekperxC .51.2$2/)00135.0105()2( =+=
4.4.1.3 Modeling of the optimal spare parts preventive replacement age and constant failure interval
4.55
Where,
EN(T,tp) is the expected number of spares required over the planning
period, when preventive replacement occurs at time tp
=Number of preventive replacements in interval (0, T)
+ Number of failure replacements in interval (0, T)
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tp is the optimal preventive replacement time(age or period).
T is the planning period
F(t) is the probability density function
[1-R(tp)] is the probability of the failure
M(tp) is the number of expected replacements in tp
Example: If T= 52weeks (1year),tp is 4 weeks, F(t) is 0.16,M(tp)=3.17
EN(52,4)=52/4(4x0.84+3.17x0.16)=13.44 per year
4.4.1.4 Modeling the optimal inspection frequency
Optimal inspection frequency to maximize profit
⎟⎠⎞
⎜⎝⎛
++
−=RVIV
in µλ )(` 4.56
Where,
‘λ(n) is d/dn λ(n)=k/n, k is constant and is the arrival rate of
breakdown per unit time, when 1 inspection is made per unit time
⎟⎠⎞
⎜⎝⎛
++
=IVRVikn
µ 4.57
n is the optimal inspection frequency.
1/µ is the mean time to repair=k
1/i is the mean time to perform inspection
I is the average cost of inspection per uninterrupted unit of time
R is the average cost of repairs per uninterrupted unit of time
V is the profit value if there are no downtime loses.
1/λ is MTTF
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)(nλλ ≡
Example: K=0.033 month
1/i=0.011 month
V=$30000
R=$250
I=$125
006.31253000025030000
011.0033.03
=⎟⎠⎞
⎜⎝⎛
++
=xn
And for optimal inspection frequency to minimize downtime
innnD +=
µλ )()( 4.58
Where, D(n) is assumed to be a continuous function of n
µkin = 4.59
Figure 52: Optimal inspection frequency to maximize profit
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Figure 53: Optimal inspection frequency to minimize downtime
4.4.1.5 Availability
MTTRMTBFMTBFA +=
4.5 Obtaining the optimized system results
We will select the new maintenance policies by simulating the
existing policies and optimize them using the models in (4.4.1.1-5),
after optimizing these policies we will compare their relevant CBR,
SBR, and OBR ratios, and we select the ones that meets the criteria of
less than 1 ratio.
Appendix 8 shows the new selected maintenance policies, and
appendix 9 shows the new selected maintenance policies, and their
effect on the system.
Re-evaluating the system with the new policies, we obtain the
followings:
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Table 44 shows the system effects data after optimization
through the system lifetime, which is set at 8760 hrs.
Table 44: Press system affects data after optimization
Table 45 shows the system profile after optimization through the
system lifetime, which is set at 8760 hrs.
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Table 45: System profile after optimization
Appendix 9 shows the mean unavailability over lifetime, number
of expected failures, total downtime, unavailability and unreliability at
lifetime (8760hrs) of the system components after optimization.
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Figure 54: Unavailability profile of plant sub-system after optimization
From evaluating the system after applying the new maintenance
policies, we get the following results:
Labor costs: $217,697.13
Spare usage costs: $6,180.00
Miscellaneous costs: $90.0
Outage costs: $2,258,064.00
Total costs: $2,482,031.13
Figure 55 shows the cost profile of the system after optimization
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Figure 55: System cost profile after optimization
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CHAPTER FIVE - SENSITIVITY ANALYSIS 5. 5.1 Introduction
The cost based optimization process is based on evaluating the
existing maintenance strategies and calculating the criticality values,
which represents the severity of the effects associated with the cause
combined with its frequency of occurrence, the next step will then be to
examine these values, any value equals one or above is considered
not beneficial from the point of view of; cost, safety/environmental and
operations, after which is to select new maintenance strategies for the
items that have values more than one, and examine their effect on
these critical values.
5.2 Comparing system data with and without dependencies before optimization
We will estimate the system reliability and availability after
including the dependencies and compare the results with the ones
obtained from theoretical assessment using the same equations used
for assessing the system reliability and availability theoretically.
5.2.1 Estimating the simulated system reliability
1#1#/.11# .. nrvvvThrustehppassyhpp RRRR =
28.092.042.074.01# == xxR assyhpp
72.028.011# =−=assyhppF
Since the HPPs are identical and independent. Hence the
reliability of them are also identical.
Therefore;
assyhppassyhppassyhppassyhpp RRRR 4#3#2#1# ===
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The assembly is an identical and independent parallel
(K-Out of-n).
Therefore the reliability of such system is:
FPPRHPP34
)41( 4+=−
70.072.028.0428.0 34)41( =+=− xxRHPP
HPPs 1-4 are in parallel with HPP no.5, therefore;
( )∏=
−−=2
1
11i
HPPs RiR
( ) )]72.01).(70.01[(1112
1
−−−=−−= ∏=i
HPPs RiR
916.0=HPPsR
• Low pressure pumps sub-assembly The 2 low-pressure pumps are in parallel
( )∏=
−−=2
1
11i
LPPs RiR
)]69.01).(69.01[(1 −−−=LPPsR
904.0=LPPsR
And they are in series with solenoid S1, valve no.8 and change
over valve no.5.
329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS RRRRRRRR =
91.091.078.078.095.091.0904.0 xxxxxxRLPPsSYS =
414.0=LPPsSYSR
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• Hydrauphore sub-assembly
( )∏=
−−=2
111#//tan/tan.. ].11.[.
ivvvireliefevkHydrphoreCompAkStassyHydrphr RRRRRR
)]81.01)(81.01[(1952.097.089.097.0. −−−= xxxxR assyHydrphr
768.0. =assyHydrphrR
• Heating system
snsrTleakPpipesLboilersysH RRRRR ///. ...=
391.094.085.071.069.0. == xxxR sysH
Remaining sub-assembly
syspipngsnsrptimerPLC
assybvvassyavvnrvvvRassy
RRRR
RRRRR
..
.7..7.6#/
....
)}].1)(1{(1)}].[1)(1{(1[ −−−−−−=
47.7.7 Savvassybvvassyavv xRRRR ==
828.091.091.0.7.7 === xRR assybvvassyavv
970.0)]828.01)(828.01[(17# =−−−=VVR
994.0)]98.01)(72.01[(1.6# =−−−=AssyVVR
656.099.078.099.089.0970.0994.0. == xxxxxxR assyR
FPPR AssyMgntBlkng23
.. 3+=
633.031.069.0369.0 23.. =+= xxR AssyMgntBlkng
)}]1)(1(
)1)(1{(1.[
.
/...
fanExTGs
poPRamsassymgntBlkngpress
RR
RRRR
−−
−−−=
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62.0)}]90.01)(42.01)(54.01)(46.01{(1[633.0 =−−−−−= xRpress
Now that the complex configuration has been reduced to a simple
assembly, we can estimate the whole system reliability.
essRassysysHeatingAssyLPPsAssyHPPssystemeHydrauphorplantP xRxRxRxRxRRR Pr..... =
62.0656.0391.0414.0916.0768.0. xxxxxR plantP =
046.0. =plantPR
5.2.2 Estimating the simulated system Availability
• High pressure pumps HPPs All the pumps are identical, so are their thrusters and non-return
valves.
1#1#/.11# .. nrvvvThrustehppassyhpp AAAA =
64.096.076.088.01# == xxA assyhpp
Since the HPPs are identical and independent, hence the
availability of them is also identical.
Therefore;
assyhppassyhppassyhppassyhpp AAAA 4#3#2#1# ===
The assembly is an identical and independent parallel
(K-Out of-n).
Therefore the Availability of such system is:
93.012.088.0488.04 3434)41( =+=+=− xxUAAAHPP
HPPs 1-4 are in parallel with HPP no.5, therefore;
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( )∏=
−−=2
1
11i
HPPs AiA
( ) )]82.01).(93.01[(1112
1
−−−=−−= ∏=i
HPPs AiA
99.0=HPPsA
• Low pressure pumps sub-assembly The 2 low-pressure pumps are in parallel
( )∏=
−−=2
1
11i
LPPs AiA
)]78.01).(78.01[(1 −−−=LPPsA
95.0=LPPsA
And they are in series with solenoid S1, valve no.8 and change
over valve no.5.
329#/8#/5#/1 ...... SSvvvvvvslppsLPPsSYS AAAAAAAA =
92.092.091.091.096.092.095.0 xxxxxxALPPsSYS =
59.0=LPPsSYSA
• Hydrauphore sub-assembly
( )∏=
−−=2
111#//tan/tan.. ].11.[.
ivvvireliefevkHydrphoreCompAkStassyHydrphr AAAAAA
)]99.01)(99.01[(198.099.093.097.0. −−−= xxxxA assyHydrphr
87.0. =assyHydrphrA
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• Heating system
leakPlatensnsrTpipesLboilersysH AAAAA .//. ...=
48.09.094.066.086.0. == xxxA sysH
• Remaining sub-assembly
syspipngsnsrptimerPLC
assybvvassyavvnrvvvRassy
AAAAAAAAA
..
.7..7.6#/
....)}]1)(1{(1)}].[1)(1{(1[ −−−−−−=
47.7.7 Savvassybvvassyavv xAAAA ==
86.092.094.0.7.7 === xAA assybvvassyavv
98.0)]86.01)(86.01[(17# =−−−=VVA
999.0)]961.01)(999.01[(1.6# =−−−=AssyVVA
UAAA AssyMgntBlkng23
.. 3+=
84.025.075.0375.0 23.. =+= xxA AssyMgntBlkng
67.099.079.099.091.098.098.0. == xxxxxxA assyR
)}]1)(1)(1)(1{(1.[ ./... fanExTGspoPRamsAssyMgntBlkngpress AAAAAA −−−−−=
75.0)}]90.01)(86.01)(77.01)(89.01{(1[75.0 =−−−−−= xApress
Estimating the system Availability
essRassysysHeatingAssyLPPsAssyHPPssystemeHydrauphorplantP xAxAxAxAxAAA Pr..... =
75.067.048.059.099.087.0. xxxxxA plantP =
12.0. =plantPA
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Using the same calculation, but substituting the values obtained
from the optimization simulation, we obtain the following results;
1. System reliability: 0.34
2. System availability: 0.30
Table 46: comparison of system reliability & availability with and without dependencies,
before and after optimization
By comparing the system reliability and availability with
dependencies and without them as shown in table 46, we can see that
there is a substantial difference between the reliability and availability
of both assessments. This highlights the effect of the dependencies on
the system.
Also by comparing the results before and after optimization and
with dependencies, we can see clearly the effect of the new proposed
condition based maintenance strategies in enhancing the system
overall reliability and availability.
We have to mention that the results of the optimization does not
take into consideration the low pressure pumps system, as they will be
redesigned, therefore we cannot estimate their values. So far there are
no models that can incorporate the dependencies such as; the cost of
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various equipment that are used to carry out the maintenance task, the
hourly rate of the labor, the number of labor used for each task, and
the number of spare parts used. Its associated costs and the time
taken to mobilize and carry out the maintenance task, all together into
a single model, hence, simulation is closest method to carry out the
assessment.
The low-pressure pumps were not included in the calculations of
reliability and availability after optimization, because the simulation
results suggested the redesign of these pumps. This is in line with the
fact that these pumps flagged for redesign for few years now by the
management.
Appendix 11 illustrates the reliability and availability of the
different system components before and after optimization.
5.3 Comparing the number of failures and downtime
The effect of the number of failures and subsequently the total
downtime of the different components of the system is proportional to
the cost of maintaining the system. Table 47 illustrates the effect of
optimization on reducing the number of expected number of failures
and the subsequent reduction in expected downtime; it clearly shows
the effect of the new maintenance strategies on the system behavior
and the substantial reduction in the number of expected failures and
system downtime.
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Table 47: the number of expected failures and downtime of the System before and after optimization
5.4 Criticality values estimation
To determine how effective (beneficial) each maintenance task is
we calculate the criticality values, which represents the severity of the
effects associated with the cause combined with its frequency of
occurrence. The following equations illustrate how these values are
calculated for each failure mode:
4) Cost Benefit Ratio (CBR).
alarmsandtasksspecifiedwithoutCostalarmsandtasksspecifiedwithCostCBR.....
.....=
A cost benefit ratio less than 1 indicates that the
task/alarm is worthwhile from a cost point of view.
5) Safety/Environmental Ratio(SBR).
alarmsandtasksspecifiedwithoutycriticalitSafetyalarmsandtasksspecifiedwithycriticalitSafetySBR......
......=
A safety/environmental benefit ratio less than 1
indicate that the task/alarm is worthwhile from a
safety/environmental point of view.
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6) Operational Benefit Ratio. (OBR).
alarmsandtasksspecifiedwithoutycriticalitlOperationaalarmsandtasksspecifiedwithycriticalitlOperationaOBR......
......=
An operational benefit ratio less than 1 indicate that
the task/alarm is worthwhile from a operational point of
view.
Appendices 6 and 9 show these values before and after
optimizing the system (applying the new maintenance strategies).
5.5 Comparing the different cost associated with the system before and after optimization
By comparing the two results obtained before and after system
optimization, we calculate the following cost saving from optimization:
1) Labor cost savings:
$96,833.10 - $217,697.13
=-$120,864.00
2) Spare parts usage savings: $7,383.00- $6,180.00
=$1,203.00
3) Miscellaneous cost savings: $2,503.21 - $90.00
= $2,413.21
4) Effects cost savings: $3,157,270 - $2,258,064
= $899,206
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Estimated Total cost savings= Labor cost savings+ Spare parts
usage savings+ Effects cost savings
=-120,864 + 1,203 + 2,413.21 + 899,206
= $781,958.18
5.6 Conclusions The system use mix maintenance strategies, with the emphasis
being on scheduled maintenance strategies, this approach has proved
to be un useful in terms of analysis and in real life application. The new
approach maintains the mix strategies policy, but puts the emphasis on
the inspection. From the above analysis, it’s clear that although there
is an increase in the labor costs, and increase in the frequency of the
inspection times, but in return;
• It has reduced the frequency of the scheduled or preventive
maintenance strategies, extending the times between these
tasks.
• The savings in the effects cost is high, which in turn yields a
positive savings of about %16 from the annual maintenance
budget.
With this reduction in the maintenance cost, it brings the budget
in line with the business objectives.
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CHAPTER SIX – CONCLUSIONS
6.1 Introduction In this study, a cost based reliability program for optimizing the
performance of a complex fiberboard pressing plant introduced, using
the Monte- Carlo method, which utilizes stochastic simulation models
as the platform for evaluating the existing system based on its current
maintenance strategies and their effects on the system at different
levels of this system. This platform has enabled the creation of plant
model that incorporates the system parts at different levels of the
hierarchy, with its dependencies, and analyzes their behavior in the
future. By knowing those behaviors we can use both engineering
judgment and the software features to introduce new maintenance
strategies such as, run to failure strategy, preventive strategies,
condition based, inspection, redesign etc, which will help reduce the
ever increasing maintenance cost and plant Unavailability.
The plant's operating concept and most of its equipment are over
50 years old; hence, its maintenance strategies have not changed
much since, although few systems have been introduced to modernize
it. With the Challenges facing this industry, this program along with the
new CMMS introduced by the researcher as a first step towards
establishing a sound maintenance program in the plant to replace the
outdated and limited functionality system, provides a good tool for the
maintenance personnel to make the right decision concerning their
equipment, as it can be easily carried out on the rest of the plant using
the same method of analysis, thus enabling the management to focus
on other aspects to improve the business rather than concentrating
their efforts on how to reduce the plant downtime and control the
continuously increasing maintenance budget.
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Maintenance policies based on mathematical models are much
more flexible than heuristic policies. Mathematical models can
incorporate a wide variety of assumptions and constraints, but in the
process they can become quite complex. A great advantage of the
mathematical approach is that the outcomes can be optimized.
Optimization with regard to changes in some basic model parameter
can be carried out for maximal reliability or minimal costs.
Mathematical models can be deterministic or probabilistic. Since
maintenance models are used for predicting the effects of
maintenance in the future, probabilistic methods are more appropriate
than deterministic ones, even if the price for their use is increased
complexity and a consequent loss in transparency. For these reasons,
the use of such methods is spreading only slowly.[195].
6.2 Major findings
The woodchip and pulp or engineered wood industry in Australia
and around the world is a lucrative industry; the industry's turnover in
Australia was $9.91 billion, or around one per cent of GDP in 1992-93
(latest available data). The industry employs approximately 82,500
people, according to the latest labor force estimates from the
Australian Bureau of Statistics. It’s a mature industry with a strong
market. Here in Australia, the Australian timber industry is going
through unprecedented change. There are significant opportunities for
growth in the production and sales of high value timber products in all
Australian species groups [1].
The predicted volume of hardwood pulpwood produced in
Australian plantations will increase from around 0.7 million cubic
meters per annum in the 1995-99 period to over 10 million cubic
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meters per annum in the period 2035-39 [3]. The increase in the
population, the rising demand for the wood products to be used in the
housing industry [4], and since hardboard has established itself as a
reliable product for use in the dwelling construction, furniture and
cabinetry industry for its unique characteristics, it is forecast that it still
can retain its niche market if it can introduce new technologies and
reduce its maintenance cost.
Industry related maintenance research and study:
• The Hardwood (engineered wood) industry uses the wet
process, this type of processes requires an extensive and high
cost maintenance, combined with its ageing equipment have
saw a continuously rising maintenance cost (about 40% of the
operation cost). The candidate study of literature revealed no
studies has been done (or at least has been published) that
addresses the issue of maintenance in this industry.
• Inspection strategies Frequency:
The key factors that this study is based on to determine
how effective (beneficial) each maintenance task is, are; the
cost benefit ratio (CBR), safety benefit ratio (SBR) and
operational benefit ratio (OBR). A benefit ratio of less than one
indicates that the maintenance task/alarm is worth doing. These
values are the criticality values, which represents the severity of
the effects associated with the cause and combined with its
frequency of occurrence for each failure mode. The study
results shows that by increasing the inspection policies
frequency over the calculated system life time (in this instance
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8760 hrs), these benefit ratios are decreased noticeably,
although this increases the cost of these inspections but the
overall result is reduction in the total maintenance cost.
• The effects of the use of mix maintenance strategies
on the expected number of failures:
The system under study uses the following mixture of maintenance policies to maintain it;
• Breakdown maintenance
• Preventive maintenance
• Statutory Maintenance
• Condition based maintenance
With the higher emphasis are being on the preventive
maintenance strategies. Although these strategies maintains the
system to a certain extend but they are timely consuming,
financially exhausting and labor intensive practices. To be able
to optimize the system and obtain better results that can
increase the availability and reliability of the system, the
research findings suggest the reduction of the preventive
strategies and increase in the condition based strategies.
Analyzing the obtained results shows that some of the
preventive strategies when replaced with inspection or other
form of condition based maintenance in some cases, and
combined the two strategies but with increase of CBM
frequency in other cases, yields the results of 56% reduction in
expected number of failures, or about 275 failures, and overall
reduction in downtime of about 50%, or about 438 hrs and as
shown in appendix 11.
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6.3 Limitations and Uncertainties:
• Although this type of industry has been around since the fifties,
so it’s considered as a mature industry, but so far there is no
literature available, this have made it hard for any researcher to
identify the type of work that has been carried out to develop
this industry.
• The absence of real life data for some of the equipments. This
has forced the candidate to rely on the memory recall of events
by the available staff and their accuracy of these events.
• Some data were found on hard copies as a remarks made by
different maintenance managers and engineers, who managed
the maintenance department throughout the years. This was a
tedious and time consuming process to convert and cleans this
information to a useful data.
• The level of accuracy of the simulation relies heavily on the
availability and credibility of the data.
• Due to the similarity of some of the equipment, an
exchangeable or rotatable spare parts permanent situation was
created, and with absence of maintenance planning and proper
documentation of spare parts rotation & movement and
traceability history, it was difficult to optimize the maintenance
strategies, spares and its replacement.
• Mathematical and statistical models were used to assess the
system and optimize the maintenance cost. Some of these
models are restricted; others are impractical and rely heavily on
assumptions.
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• The sensitivity of some of the business performance data and
its confidentiality, have limited the use of it, hence jeopardized
the integrity of the research.
• Some of the equipment failure data was recorded on equipment
level. With the absence of low level (parts or component level)
history data, made it difficult to analyze this equipment at a
lower level and to describe the interaction among these parts or
components.
• The simulation software utilizes fault tree analysis. In FTA
components at low level can affect higher levels only and not
vice versa, hence the components interactive failures at same
level are uncertain and hence cannot be described.
• The lifetime measure of items is uncertain, hence the failure
times are very much randomly variable.
• A degree of uncertainty of the cost arises from the uncertainty of
the maintenance decision taken during coverage time.
• There is some uncertainty about some of the lost time recorded
against the system, but in fact it was outside the system
boundary. Due to the complexity and irregularity in recording the
lost times in the plant database, it was difficult to verify and
exclude these data from the actual system in concern.
• We are also cautious about the simulation results obtained after
optimization for the system reliability and availability, as the
calculations did not include the low pressure pumping system,
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due to the fact that it is suggested to redesign the low pressure
pumps, therefore we cannot estimate their parameters for now.
6.4 Future studies
To study the interactive relationship between components
at the same level to understand how this interaction affects on
the behavior of an existing system. Once this is achieved it will
pave the way to the attempt of developing a reliability model
based on the interaction of components at the same level in a
repairable multi components system, this will enhance the
accuracy of FTA by simulation in association with the simulation
software provider. The conventional models of dependant
failures do not cope at all with interactive failures, which are the
failures caused by interaction between different components[38]
The introduction of multi inventory Production Scheduling
(MIPS) method for a system that uses a mixed maintenance
strategies and subject to imperfect repairs.
The issue of production rate, availability of the machines,
set up time & other production issues and it’s relation with
maintenance have become a centre of focus recently. Gur
Mosheiov [196] stated “Scheduling a maintenance activity of the
machines in production systems has become an important
decision for both practitioners and researchers. Often, a
maintenance must be performed within a given time interval.
During the maintenance time, the machine is shut down, and
the production is stopped. Consequently, the completion times
of the jobs processed after the maintenance are delayed.” He
focused on scheduling a maintenance activity on a system of
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unrelated parallel machines; his main contribution was the
introduction of a simple heuristic procedure. Liao [197] in his
study integrated maintenance and production programs with the
economic production quantity (EPQ) model for an imperfect
process involving a deteriorating production system with
increasing hazard rate: imperfect repair and rework upon failure
(out of control state), while Chung [198] focused on optimizing
the system reliability in multi factory production network by
maintenance approach, Dehayem [199] suggested a
hierarchical decision making in production and repair/
replacement planning with imperfect repairs under uncertainties,
Fei [200] introduced optimal production run time for a
deteriorating production system under imperfect repairs and
maintenance, and [201] have focused on the optimal
maintenance of a production inventory system with idle periods.
The above studies and others did not deal with the issue of
multi product inventory. Many industrial plants produce multi
products on the same production line, with change in the set up,
the variable production rate for different products and the
decision when to stop production up on discovering of failure,
the severity of the failure. All these uncertainties needs to be
dealt with under one approach, which the candidate believes it
can be a useful future project.
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APPENDIXES
APPENDIX 1 COMPONENTS THEORETICAL UNRELIABILITY, RELIABILITY AND
AVAILABILITY (WITHOUT DEPENDENCIES
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APPENDIX 2 SYSTEM HIERARCHY
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APPENDIX 3 LIST OF THE PRESSING PLANT LABOUR
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APPENDIX 4 SPARE PARTS LIST
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APPENDIX 5 CURRENT MAINTENANCE POLICIES AS EXTRACTED FROM THE PLANT CMMS
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APPENDIX 6 EXISTING MAINTENANCE POLICIES’ EFFECT ON THE SYSTEM
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APPENDIX 7 SYSTEM COMPONENTS DATA BEFORE OPTIMIZATION
Description
Number of expected failures before optimization
Total downtime before optimization
Availability at lifetime before optimizatio
Reliability at lifetime before optimization
BOILER 13.60 40.80 0.86 0.69 HPP#1 21.30 31.95 0.88 0.74 HPP#5 14.82 22.23 0.82 0.72 HPP#2 21.30 31.95 0.88 0.74 HPP#3 21.30 31.95 0.88 0.74 HPP#4 21.30 31.95 0.88 0.74 I/L NRV 19.30 14.47 0.98 0.98 L/PIPES 32.76 16.38 0.66 0.71 NRV#1 11.30 5.65 0.96 0.92 NRV#2 11.30 5.65 0.96 0.92 NRV#3 11.30 5.65 0.96 0.92 NRV#4 11.30 5.65 0.96 0.92 P/SENSOR 7.20 5.13 0.78 0.78 PIPING SYS 1.70 0.85 0.99 0.99 PLTN/LEAK 23.65 35.47 0.90 0.85 S10 6.70 6.70 0.92 0.91 S4 6.30 6.70 0.92 0.91 T/GUIDES 14.30 35.75 0.86 0.42 T/SENSOR 4.60 3.20 0.94 0.94
THRUSTER VLV#1 15.80 15.80 0.76 0.42
THRUSTER VLV#2 15.80 15.80 0.76 0.42
THRUSTER VLV#3 15.80 15.80 0.76 0.42
THRUSTER VLV#4 15.80 15.80 0.76 0.42
TIMER 15.30 8.20 0.99 0.99 V/V NO.6 5.60 19.60 0.89 0.72
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VLV7a 5.40 5.21 0.94 0.91 VLV7b 5.40 5.21 0.94 0.91 V/V#11. 5.30 11.54 0.98 0.95 LOW P
PUMPS 11.27 16.90 0.78 0.69 S2 5.70 13,87 0.92 0.91 PLC 8.20 12.30 0.91 0.89 S3 5.70 13,88 0.92 0.91 V/V#9 2.60 7.80 0.91 0.78 V/V#8 2.60 7.80 0.91 0.78 V/V#5 5.30 7.95 0.96 0.95 BLK MGNT# 8.90 8.90 0.75 0.69 BLK MGNT# 8.90 8.90 0.75 0.69 BLK MGNT# 8.90 8.90 0.75 0.69 A/COMP 8.70 10.87 0.93 0.89 RELIEF
V/V#1 0.32 11.71 0.99 0.81 HYPHR TK 2.59 8.34 0.99 0.97 RELIEF
V/V#2 0.25 9.45 0.99 0.81 NRV 19.65 126.33 0.97 0.97 RAMS 1.30 93.60 0.89 0.46 PLATENS
O/P 11.40 92.00 0.77 0.54
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APPENDIX 8 NEW MAINTENANCE POLICIES TO BE EXPORTED TO THE PLANT CMMS
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APPENDIX 9 NEW MAINTENANCE POLICIES’ EFFECTS ON THE SYSTEM AFTER OPTIMIZATION
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APPENDIX 10 SYSTEMS COMPONENTS DATA AFTER OPTIMIZATION
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APPENDIX 11 System components analysis before and after optimization
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APPENDIX 12 The system Failure Mode effects and criticality analysis (FMECA)
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