Importance of Modeling
& Simulation
Throughout In-service
Lifecycle Phase
Leigh Jarman
Senior 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
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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?
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Example 1
• Maintenance task 1 –
– Function test valve
– Weekly interval
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January
Week 1
Week 2
Week 3
Week 4
February
Week 1
Week 2
Week 3
Week 4
March
Week 1
Week 2
Week 3
Week 4
April
Week 1
Week 2
Week 3
Week 4
4
• Click to edit Master text styles
– Second level
• Third level
– Fourth level
» Fifth level
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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.
–
–
–
–
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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?
•
•
•
•
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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.
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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”
Characteristic life of 38818
hours with a shape curve of
0.80. – infant mortality
Characteristic life of 31520
hours with a beta shape
curve of 3.3. – wear out
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Case Study 1
“Know Your Equipment”
Characteristic life of 17846 hours
with a beta shape curve of 0.54. –
infant mortality
Characteristic life of 23946 hours with
a beta shape curve of 1.1. – best
when new (not quite random)
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In-service Simulation Case
Studies
Failure data is displaying three
possible types of failure mode
and data requires a more
detailed investigation
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Case Study 1
“Know Your Equipment”
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
Installation 8
Installation 9
Infant mortality
Original
25033
0.4
Installation 10
Infant mortality
Original
Installation 11
20073
0.4
Infant mortality
Installation 12
10946
0.91
Infant mortality
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Case Study 1
“Know Your Equipment”
• 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.
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• 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.
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• 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.
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• Maintenance Strategy Simulation 1
• Complete inspection at current interval – 4 wkly
using assumed wear rate.
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• Maintenance Strategy Simulation 2
• Optimise task interval based on current production
and assumed wear rate.
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• 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.
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• Maintenance Strategy Simulation 3
• Optimise task interval based on failure data
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• 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.
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• Maintenance Strategy Simulation 4
• Optimise task interval based on future production
rates
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• Maintenance Strategy Simulation 5
• Optimise task interval based on adjusted future
production rate. (Factor = 1.30)
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Case Study 2
“Predict Today & Forecast for Tomorrow”
• Maintenance Strategy Simulation Results
Simulation 1 Assumed Wear rate
Assumed wear rate
Simulation 2 optimised
Actual failure data
Simulation 3 optimised
Adjusted future failure
Simulation 4 rate
Readjusted future failure
Simulation 5 rate
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Insp
downtime
No Insp's
PM
downtime
No PM's
Cost
132
66
264.4
6.66
$449,991
30
15
264.4
6.61
$440,811
132
66
132
4.84
$332,675
132
66
320.98
8.03
$543,950
132
66
264
6.61
$449,747
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Potential Issues With In-service
Strategy Simulation
• 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.
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Summary
• 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.
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