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

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 30/04/20102

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? 30/04/20103

4. Example 1 Maintenance task 1 – – Function test valve – Weekly interval 30/04/20104 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

5. Click to edit Master text styles – Second level Third level – Fourth level » Fifth level 30/04/20105

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. 30/04/20106

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. 30/04/20107

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. 30/04/20108

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 30/04/20109

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. 30/04/201010

11. Maintenance Strategy Development Many software packages available to assist in maintenance strategy development and simulation. Step through traditional 7 questions of RCM. 30/04/201011

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? 30/04/201012

13. Maintenance Strategy Development How many questions and assumptions can change throughout the in-service phase of equipment life? 30/04/201013

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? 30/04/201014

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? 30/04/201015

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. 30/04/201016

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 30/04/201017

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. 30/04/201018

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. 30/04/201019

20. Case Study 1 “Know Your Equipment” 30/04/201020

21. Case Study 1 “Know Your Equipment” 30/04/201021 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

22. Case Study 1 “Know Your Equipment” 30/04/201022 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

23. In-service Simulation Case Studies 30/04/201023 Failure data is displaying three possible types of failure mode and data requires a more detailed investigation

24. Case Study 1 “Know Your Equipment” 30/04/201024 Failure Analysis Summary InstallationRunning HoursEta (Hours)Beta (Shape)Comments/Action Installation 1 388180.8 Infant mortality Installation 2 315203.3 Wear out Installation 3 269931.1 Best when new almost Random Installation 4 239461.1Best when new almost Random Installation 556612 Still running Installation 633168 Still running Installation 7 530000.48 Infant mortality Installation 8 Original Installation 9 250330.4 Infant mortality Installation 10 Original Installation 11 200730.4 Infant mortality Installation 12 109460.91 Infant mortality

25. Case Study 1 “Know Your Equipment” 30/04/201025 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 ” 30/04/201026 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 ” 30/04/201027 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 ” 30/04/201028 Maintenance Strategy Simulation 1 Complete inspection at current interval – 4 wkly using assumed wear rate.

29. Case Study 2 “ Predict Today & Forecast for Tomorrow ” 30/04/201029 Maintenance Strategy Simulation 2 Optimise task interval based on current production and assumed wear rate.

30. Case Study 2 “ Predict Today & Forecast for Tomorrow ” 30/04/201030 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 ” 30/04/201031 Maintenance Strategy Simulation 3 Optimise task interval based on failure data

32. Case Study 2 “ Predict Today & Forecast for Tomorrow ” 30/04/201032 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 ” 30/04/201033 Maintenance Strategy Simulation 4 Optimise task interval based on future production rates

34. Case Study 2 “ Predict Today & Forecast for Tomorrow ” 30/04/201034 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 ” 30/04/201035 Maintenance Strategy Simulation Results Insp downtimeNo Insp's PM downtimeNo PM'sCost Simulation 1Assumed Wear rate13266264.46.66$449,991 Simulation 2 Assumed wear rate optimised3015264.46.61$440,811 Simulation 3 Actual failure data optimised132661324.84$332,675 Simulation 4 Adjusted future failure rate13266320.988.03$543,950 Simulation 5 Readjusted future failure rate132662646.61$449,747

36. Potential Issues With In-service Strategy Simulation 30/04/201036 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 30/04/201037 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.