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Importance of Modeling & Simulation
Throughout In-service Lifecycle Phase
Leigh JarmanSenior Reliability Engineer
Importance of Modeling and Simulation throughout In-service Lifecycle Phase
• Presentation Outline– Introduction– Maintenance strategy development and
integration of change.– Case Study 1
“Know Your Equipment”
– Case Study 2“Predict Today & Forecast for Tomorrow”
– Potential issues with in-service strategy simulation
30/04/2010 2
Introduction
• How do we know that what we are doing and when we are doing it is right?
• How do we produce a meaningful maintenance strategy?
30/04/2010 3
Example 1
• Maintenance task 1 – – Function test valve – Weekly interval
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JanuaryWeek 1Week 2 Week 3 Week 4FebruaryWeek 1Week 2Week 3Week 4March Week 1Week 2Week 3Week 4April Week 1Week 2Week 3Week 4
• Click to edit Master text styles– Second level
• Third level– Fourth level
» Fifth level
30/04/2010 5
Maintenance Strategy Development
• Maintenance strategy development can occur at any time during a project life cycle.– New Projects
– Greater opportunity for total lifecycle cost saving.
– Existing Projects– Greater opportunity for optimisation through use of
historical data.
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Maintenance Strategy Development
• Objective is to– Shifts the focus from fixing failures to
preventing failures.– Achieve dependable asset performance
that is responsive to organisational controls.
– changes in the business climate,– changing priorities,– as failure patterns emerge,– as new technology becomes available.
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Maintenance Strategy Development
• Simulation and forward predictions allow;– Likely failures are documented based on experience, local
plant knowledge, industry guides, and historical records.– Maintenance tasks are selected to address likely failures
and reduce the effects of failure. – Existing maintenance strategies can be imported and
optimised.– Models are used to simulate decisions on the computer
desktop prior to implementing in the field.– The effects of redundancy, resource costs, equipment
ageing and repair times must be taken into account.
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Maintenance Strategy Development
Simulation and forward predictions allow optimization in;– Identification of critical items and risk.
– Maintenance tasks at optimum frequencies.
– resource allocation (spares, labour, equipment),– budgeting decisions
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Maintenance Strategy Development
• Simulation and forecasting for new projects– Assumptions must be made for analysis;
– Effects of failure,– Failure rates based on type of product and
production rates,– Like equipment ,– Experience & engineering judgement,– OEM & Industrial publications.
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Maintenance Strategy Development
• Many software packages available to assist in maintenance strategy development and simulation.
• Step through traditional 7 questions of RCM.
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Maintenance Strategy Development
• 7 questions of RCM;• What is the function of the equipment / component?
• What functional failures could occur?
• What are the causes to each functional failure?
• What happens when the failure occurs?
• How does this failure matter, ie significance of the
failure?
• What should be done to predict or prevent the failure?
• What should be done if no suitable task exist, i.e. RTF or
redesign?
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Maintenance Strategy Development
• How many questions and assumptions can change throughout the in-service phase of equipment life?
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Maintenance Strategy Development
• Do these change?• What is the function of the equipment / component?
• Does the equipment do the same as what it was designed?• Has the requirements changed?
• What functional failures could occur?• How is not performing?
• What are the causes to each functional failure?• Has new failures emerged?• Is it failing quicker than first estimated? Are the conditions of
operation same as designed?• Has any engineering changes occurred to alter performance?
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Maintenance Strategy Development
• Do these change?• How does the failure matter?
• Are the environmental effects the same as designed?• Increase in community and media exposure?• Is production losses more costly?
• What happens when the failure occurs?• Are the remedial tasks the same?• Is the resources the same cost and availability?
• What should be done to predict or prevent the failure?• Can a new task be indentified?• Are new NDT or Condition Monitoring technologies available?• Refine OEM recommendations to site specific conditions?• Is it worth doing still?
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Maintenance Strategy Development
• Systematic review of maintenance strategies during in-service phase of equipment life allows;• Failure data utilization to predict failures more accurately.• Update regularly based on changes in business
environment,• Changes in labour/spares/equipment costs• Changes in effects (product costs and rates)
• Maintenance strategy is dynamic and can be refined as business needs change.
30/04/2010 16
In-service Simulation Case Studies
• 2 case studies;– “Know Your Equipment”
– Simulation of actual failure data to understand equipment performance
– “Predict Today & Forecast for Tomorrow”– Using in-service data to predict lifecycle costs
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Case Study 1 “Know Your Equipment”
• Failures present an opportunity to learn something about the behavior of the component.
• By analyzing and utilising failure data maintenance strategy decisions can be refined or challenged.
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Case Study 1 “Know Your Equipment”
• Component “A”• Multiple installations.• Assumed wear out behavior, fixed time replacement
required.• Analysis of failure history to challenge maintenance
strategy, using Weibull Module within Availability Workbench.
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Case Study 1“Know Your Equipment”
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Case Study 1“Know Your Equipment”
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Characteristic life of 31520 hours with a beta shape curve of 3.3. – wear out
Characteristic life of 38818 hours with a shape curve of 0.80. – infant mortality
Case Study 1“Know Your Equipment”
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Characteristic life of 23946 hours with a beta shape curve of 1.1. – best when new (not quite random)
Characteristic life of 17846 hours with a beta shape curve of 0.54. – infant mortality
In-service Simulation Case Studies
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Failure data is displaying three possible types of failure mode and data requires a more detailed investigation
Case Study 1“Know Your Equipment”
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Failure Analysis Summary
Installation Running Hours Eta (Hours) Beta (Shape) Comments/Action
Installation 1 38818 0.8 Infant mortality
Installation 2 31520 3.3 Wear out
Installation 3 26993 1.1 Best when new almost Random
Installation 4 23946 1.1 Best when new almost Random
Installation 5 56612 Still running
Installation 6 33168 Still running
Installation 7 53000 0.48 Infant mortality
Installation 8 Original
Installation 9 25033 0.4 Infant mortality
Installation 10 Original
Installation 11 20073 0.4 Infant mortality
Installation 12 10946 0.91 Infant mortality
Case Study 1“Know Your Equipment”
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• Component “A”• Assumed wear out• Dominate failure type – Infant mortality.• Recommendation – complete Root Cause Analysis• Actions –
• Root Cause Analysis completed.• Re-engineered issue from component.
Case Study 2“Predict Today & Forecast for Tomorrow”
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• Case study illustrates how in service failure data can affect maintenance strategy forecasting.
• Use of this data to illustrate effect on strategy against change in business directions.
• For simplicity will consider 1 failure mode on conveyor belt.
Case Study 2“Predict Today & Forecast for Tomorrow”
30/04/2010 27
• Consider “Conveyor belt fails due to wear”• Failure Effects – Production downtime• Assumed failure rate set at 7633 hours from assumed
wear rate.• 7 MTBO values from analysis of historical records.• Corrective, planned and inspection maintenance
tasks set. Assumed full belt replacement required with belt thickness testing inspection selected.
• Simulation completed over 5 years.
Case Study 2“Predict Today & Forecast for Tomorrow”
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• Maintenance Strategy Simulation 1• Complete inspection at current interval – 4 wkly
using assumed wear rate.
Case Study 2“Predict Today & Forecast for Tomorrow”
30/04/2010 29
• Maintenance Strategy Simulation 2• Optimise task interval based on current production
and assumed wear rate.
Case Study 2“Predict Today & Forecast for Tomorrow”
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• Maintenance Strategy Simulation 3• Optimise task interval based on failure data
Characteristic life of 10220 hours with a beta shape curve of 1.66 – slight wear out, nearly random.
Case Study 2“Predict Today & Forecast for Tomorrow”
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• Maintenance Strategy Simulation 3• Optimise task interval based on failure data
Case Study 2“Predict Today & Forecast for Tomorrow”
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• Maintenance Strategy Simulation 4• Optimise task interval based on future production
rates• Assume an increase on wear proportional to
increase on tonnage, increase on utilisation and increase on availability.
• Assumed factor is set to 1.62• Assumed belt life reduction from 10 220 hrs to
6308 hrs.
Case Study 2“Predict Today & Forecast for Tomorrow”
30/04/2010 33
• Maintenance Strategy Simulation 4• Optimise task interval based on future production
rates
Case Study 2“Predict Today & Forecast for Tomorrow”
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• Maintenance Strategy Simulation 5• Optimise task interval based on adjusted future
production rate. (Factor = 1.30)
Case Study 2“Predict Today & Forecast for Tomorrow”
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• Maintenance Strategy Simulation Results
Insp downtime No Insp's
PM downtime No PM's Cost
Simulation 1 Assumed Wear rate 132 66 264.4 6.66 $449,991
Simulation 2Assumed wear rate optimised 30 15 264.4 6.61 $440,811
Simulation 3Actual failure data optimised 132 66 132 4.84 $332,675
Simulation 4Adjusted future failure rate 132 66 320.98 8.03 $543,950
Simulation 5Readjusted future failure rate 132 66 264 6.61 $449,747
Potential Issues With In-service Strategy Simulation
30/04/2010 36
• Main potential issue when trying to optimise maintenance strategy during in service phase;• Discipline –
• To ensure that failures are adequately captured and documented as to learn from their occurrence and to prevent reoccurrence.
• Data management – Work order historical data must be of quality otherwise improper judgement and conclusions will result.
• To implement change – to implement recommended changes rather than resort to old practice
• Resist urge to resort to “knee jerk” strategy - promote discussion rather than introduce new task for sake of it.
Summary
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• In-service modeling and simulation is important as;• To ensure that failures are captured and suitably
addressed.• Assumptions are accurate and a true reflection of
current performance.• Maintenance tasks are continually challenged and
refined against current performance.• Maintenance strategy is dynamic and can adapt to
changing business objectives and climate.