Importance of Modeling & Simulation Throughout In-service Lifecycle Phase

1. Leigh Jarman Senior Reliability Engineer Importance of Modeling & Simulation Throughout In-service Lifecycle Phase

2. 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

3. 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?

4. Example 1 JanuaryWeek 1Week 2 Week 3 Week 4FebruaryWeek 1Week 2Week 3Week 4March Week 1Week 2Week 3Week 4April Week 1Week 2Week 3Week 4 • Maintenance task 1 – • Function test valve • Weekly interval

5. Click to edit Master text styles • Second level • Third level • Fourth level • Fifth level

6. 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.

7. 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.

8. 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.

9. 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

10. 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.

11. Maintenance Strategy Development • Many software packages available to assist in maintenance strategy development and simulation. • Step through traditional 7 questions of RCM.

12. 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?

13. Maintenance Strategy Development • How many questions and assumptions can change throughout the in-service phase of equipment life?

14. 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?

15. 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?

16. 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.

17. 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

18. 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.

19. 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.

20. Case Study 1 “Know Your Equipment”

21. 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

22. 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)

23. In-service Simulation Case Studies Failure data is displaying three possible types of failure mode and data requires a more detailed investigation

24. Case Study 1 “Know Your Equipment”

25. 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.

26. 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.

27. 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.

28. Case Study 2 “Predict Today & Forecast for Tomorrow” • Maintenance Strategy Simulation 1 • Complete inspection at current interval – 4 wkly using assumed wear rate.

29. Case Study 2 “Predict Today & Forecast for Tomorrow” • Maintenance Strategy Simulation 2 • Optimise task interval based on current production and assumed wear rate.

30. 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.

31. Case Study 2 “Predict Today & Forecast for Tomorrow” • Maintenance Strategy Simulation 3 • Optimise task interval based on failure data

32. 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.

33. Case Study 2 “Predict Today & Forecast for Tomorrow” • Maintenance Strategy Simulation 4 • Optimise task interval based on future production rates

34. Case Study 2 “Predict Today & Forecast for Tomorrow” • Maintenance Strategy Simulation 5 • Optimise task interval based on adjusted future production rate. (Factor = 1.30)

35. Case Study 2 “Predict Today & Forecast for Tomorrow” • Maintenance Strategy Simulation Results

36. 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.

37. 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.