29.1.2009nordic process control workshop, porsgrunn, norway application of the enhanced dynamic...

29.1.2009 Nordic Process Control Workshop, Porsgrunn, Norway

Application of the Enhanced Dynamic Causal Digraph Method on a Three-layer Board Machine

Cheng Hui, Vesa-Matti Tikkala, Sirkka-Liisa Jämsä-Jounela

Helsinki University of TechnologyFaculty of Chemical Technology and Material Sciences

Research group of Process Control and Automation


Table of Contents

1. Introduction2. Fault Diagnosis Using Enhanced Dynamic Causal

Digraph Method3. Application of the EDCDG on an Industrial Process4. Description of the Process and the Test

Environment5. Fault Diagnosis Results6. Conclusions


1. Introduction

1.1 Background• Fault diagnosis methods are needed in the

paper industry due to complex processes and problems caused by the process faults

• Fault diagnosis based on causal digraph methods has been researched since 1970’s


1. Introduction

1.2 Aim of the Research• The aim of this study was to test the enhanced

dynamic causal digraph method on a board machine simulator by simulating real industrial fault scenarios– Four fault scenarios were selected, one is

presented in here


Table of Contents





2. Fault Diagnosis Using Enhanced Dynamic Causal Digraph Method

2.1 Fault Detection and Isolation• Fault diagnosis using EDCDG method is based on the

process models and the causal structure of the process– Method performs the detection by observing residuals

generated by comparing model outputs and process measurements

– The isolation is carried out by an inference mechanism which evaluates the residuals and forms a propagation path of the fault effect according to the causal structure



2.2 Dynamic causal digraph model• Describes the cause-effect relationships between the process

variables– The structure of the causal digraph is based on the prevailing causal

dependencies in the process– The cause-effect models can be described by any mathematical model

type

Cause-effect model

Pump rotation speed

Valve opening

Mass flow rate Tank level

Cause-effect model



2.3 Fault Detection• Faults are detected by

observing the global residuals generated by the comparison of measurements and simulation values

• Cumulative sum algorithm is used for detection



2.4 Fault Isolation• The origin of the fault can

be located by a recursive evaluation of local residuals– Total local residual (TLR)– Individual local residuals (ILR)– Multiple local residuals (MLR)

• The nature of the fault can be also inferred from the residuals



• In case of a process fault a further step can be taken in order to isolate the actual process component causing the fault– There are 2n -1, where n is number of input variables,

possibilities for the cause • A solution is to introduce process knowledge in a

form of a matrix M– It describes the relationships between the arcs of the

digraph



• The consistency between the suspected sets of arcs and process knowledge is checked

otherwise,0

offaultthe

causesarcfaulty,1

),( jU,

iU,

jiUM

otherwise,0

1,),(if,1)( aNiSiARCi

Msv

)()( Msvsv NUMNUM

suspected arcs sets

knowledge matrix

inference

decreased number of suspected arcs sets


Table of Contents





3. Application of the EDCDG method on an Industrial Process

• A procedure for offline testing:– Process study – Causal digraph modeling– Fault simulations/data collection– Fault diagnosis testing


Table of Contents





4. Description of the Process and the Test Environment

• The process comprises a three-layer board machine, which has two stock preparation parts and three short circulations

• The process was simulated in APROS simulation environment


Table of Contents





5. Fault Diagnosis Results

5.1 The causal digraph model of the process• A causal digraph model of the board machine was

constructed– 55 variables in total– To describe to cause-effect

relationships various modeltypes were used

• First principles models• Static regression• Neural networks

– Also the knowledge matrixM was obtained



5.2 Hydrocyclone plugging• Hydrocyclones are used for cleaning of the stock

– In some cases they are vulnerable to plugging• A fault in the hydrocyclone cleaning plant causes

– Decreased cleaning efficiency– Extra pressure loss



• The fault was simulated with the board machine model in two operation points

• Global residuals were generated based on the simulation values and the measurements

– 5 detected GRs



• Local residuals (TLR, ILR, MLR) were generated for the detected variables– The fault origin was located:

total of three origins• acceptcon2• headcon2• headflow22

• Fault was identified as a process fault



• Three nodes with 4, 3 and 2 input arcs, respectively, resulted as: sets of suspected arcs

• Process knowledge matrix M was used to decrease the number of them– Each set was tested by – If the above equation holds, the set can be accepted as a

possible result• The number of possible results decreased to 5

4 3 22 1 2 1 2 1 315

( ) ( )NUM NUM sv sv M


Table of Contents





6. Conclusions

• The Enhanced Dynamic Causal Digraph method was applied on an advanced board machine simulator and tested with four fault scenarios

• The EDCDG method provides valuable additional information for fault isolation compared to the previously presented methods– The results from each four cases were very promising

• In future the methods is going to be tested online on this board machine


Thank You for your attention!

Questions?

29.1.2009nordic process control workshop, porsgrunn, norway application of the enhanced dynamic...

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