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Asset Analytics – Zero SurprisesUsing Predictive Analytics to improve plant performance
Rob de Heus
Sitech Manufacturing Services
ChemicaInvest
... We are able to predict equipment failures and there are no surprises…
...We are able to monitor the asset health real time and make decisions based on this information…
...We are able to forecast asset life based on different scenario’s...
What if …
ZERO SURPRISES, dream or reality?That is our vision and we like to share our journey with you
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Innovation?
…we connect the dots…
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…to build Sitech’s Asset Health Center
Melding naar plant
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Asset Management Model Sitech
conf. ISO55000
2
Technology
People Process45
6
National funding:Field lab & Living lab
7
Together we are stronger!
1
Intro Sitech
Our roadmap to ZERO surprises
Maintenance Strategy
3
Case paper & pilots
8
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1. Introduction Sitech2. Asset Management3. Maintenance Strategy4. People5. Process6. Technology7. National funding program8. Cases
Together we are stronger!
Who is Sitech?
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1. Introduction Sitech2. Asset Management3. Maintenance Strategy4. People5. Process6. Technology7. National funding program8. Cases
Do you have a strategic multi year plan?
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Maximum Added Value over Life Cycle
Asset Management Model Sitech – © Sitech 2014
Strategic Partnerstrategic long term planning
Integrated Services
over Life cycle
Added ValueRisk, cash flow &
performance
Continuous Improvement
Assets, processes & people
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What is business value behind early warnings?
1. Introduction Sitech2. Asset Management3. Maintenance Strategy4. People5. Process6. Technology7. National funding program8. Cases
Do you have a strategy what to do?
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From preventive to ‘value driven’ maintenance
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Van preventief onderhoud naar “value driven” onderhoud.
Reactive Planned Preventative Pro-Active Value-Drive
Reactive
Planned
Preventive
Predictive
Pro-Active
Condition
Value DrivenM
aint
enan
ce C
ost
Time
2013 2018
ReliabilityAvailability
Integrity
Relia
bilit
y, A
vaila
bilit
y, In
tegr
ity
Start with more PdM, you need less PM, you get less CM and grow to VDM
Typical areas of risk managementin maintenance are reliability (RCMII) and integrity (RBI).
Typical areas of risks and/or opportunities are:• Safety• Health• Environment• Production losses• Costs• Quality• Security • Image
Value driven means: 1st Know your risks…
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Value based approach
overlife cycle
Performance
Risks
Cashflow
…and 2nd, know your performance… …and value drivers.13 Competences (6 live today)
for 14 plants, 250.000 ‘assets’:
1. SHEQ Control
2. Asset Utilization
3. Cost Control
4. Asset Portfolio Management
5. Reliability Engineering
6. Planning & Scheduling
7. Job Execution
8. Capital Projects
9. Skill & Tool Control
10. Spare Part Control
11. Asset Data Control
12. Outsourcing Control
13. Customer Relation Management
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Used for:• Performance Management• Value driver analyse• Benchmarking• Manual analyses• Continues Improvement
Why is predictive the strategy of the future? (1/2)
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89% of all failures have a random pattern (RCMII)• Therefor ‘time based’ is often not effective (to early or to late)• ‘Corrective’ is in many cases not desirable (firefighting, risks)• ‘Condition based’ is on the right moment (even if pattern is random)
Why is predictive the strategy of the future? (2/2)
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The developments in technology to support predictive analytics are huge* price and quality of diagnose devices* development of (wireless) sensors* speed and costs of data processing* possibilities and access w.r.t. “big data analyse”* Growth # devices, which “produce” more data (IIoT)
In chemical sector sensors are more expensive, But I expect the same trend direction
Industrial Internet of Things
Source: Ahmed Banafa
1. Introduction Sitech2. Asset Management3. Maintenance Strategy4. People5. Process6. Technology7. National funding program8. Cases
Technology
People Process
“Better alignment means more success”
Technology is the easy part…
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…we need to change our ways of working…1. Inspect/detect
2. Choose Scenario
3. Plan action
4. Execute action
5. Improve
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close the loop!
…and train & coach our people.
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…and add technology, to connect the dots:
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Melding naar plantIMPRESSION, RUNNING BEGIN OF 2016
Sitech Asset Health Center
We take small steps…
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Melding naar plant
Pilot: Machine Learning
in the cloud
3D plant scan for improvement turnarounds
Pilot: Machine Learning on premise software
333 sensors: real time vibration trending and
analysing
VDM Asset Management
Dashboard
APM: Asset strategies, tasks &
workflow
160 mobile devices
Pilot: Condition monitoring 10 control valves
Livinglabs Sitech
1. Introduction Sitech2. Asset Management3. Maintenance Strategy4. People5. Process6. Technology7. National funding program:8. Cases
Cooperation will become key in our industry…
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Participation national innovation and funding program
• National initiative: Smart Industry Netherlands: Impulse for the industry
Smart Industry
• 1 of the 10 fieldlabs (+livinglabs)
• radical target:
100% predictive maintenance
Campione
• Funding total Campione €11,5 mln
• Funding Sitech max. € 935.000 Subsidiegever
s
• Input Sitech: 10 plants Chemelot
• Therefore: Sitech biggest Livinglabof the Netherlands
Sitech
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1. Introduction Sitech2. Asset Management3. Maintenance Strategy4. People5. Process6. Technology7. National funding program8. Cases
Celebration and story telling…
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Prediction of potential failure of control valves
Identified:During a pilot we monitored the health of 10 control valves in the plant. Our specialists recognised deviations on two valves by finding patterns in the health data.One valve had a starting air leakage and the second valve a positioner in bad condition.
Action: Both valves were repaired (changed)during a small planned stop.
Added Value: By repairing the valves during a planned stop it avoided production losses. The value of the losses are in total € 135.000.
Plant Manager Diederik van Heugten:
“I’m happy with this initiative that proved this method works and with added
value results. This is just the beginning…”
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Run17
Actual Prediction
168 uur
11/02/2015 t/m 01/06/2015
7 days forecast of behaviour filter S3401
Identified:
The pollution in this filter grows very unpredictable and therefor it is
hard to plan the change over. This leads often to urgency work.
Action:
During one of the Livinglabs of Sitech we did a pilot with
Machine Learning in de cloud and developed a model to predict
and forecast the trend line for the coming 7 days.
For the pilot we made a temporary dashboard to visualise the results.
Added Value:
With this forecast it is possible to plan the filter
change over and reduces downtime and costs:
• Better to plan
• More parallel work to combine
• First step to improve live time of filter
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Zero Surprises…
…there is more possible than you might think!
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