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Optimizing preventive maintenance for mechanical components using genetic algorithms

Optimizing preventive maintenance for mechanical components using genetic algorithms. 指導教授：童超塵 老師 作者： Yuo-Tern Tsai, Kuo-Shong Wang, and Hwei-Yuan Teng 出處： Reliability Engineering and System Safety, Vol. 74, pp.89-97, 2001 報告人：陳建旻. Outline. Introduction

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Optimizing preventive maintenance for mechanical components using genetic algorithms

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  1. Optimizing preventive maintenance for mechanical components using genetic algorithms 指導教授：童超塵 老師 作者：Yuo-Tern Tsai, Kuo-Shong Wang, and Hwei-Yuan Teng 出處：Reliability Engineering and System Safety, Vol. 74, pp.89-97, 2001 報告人：陳建旻

  2. Outline • Introduction • Effect of 1P-maintenance to reliability • Maintenance benefit analysis • Genetic algorithms • Case study • Conclusion

  3. 1. Introduction (1/3) • 維護分為：矯正維護(corrective maintenance)與預防維護(preventive maintenance)。 • 矯正維護可分為：小維修(minimal repair-1C)與矯正替換(corrective replacement-2C)階段。 • 預防維護可分為：預防維護(preventive maintenance-1P)與預防替換(preventive replacement-2P)階段。

  4. 1. Introduction (2/3)

  5. 1. Introduction (3/3) 一般而言，會將系統分為個別的零組件或單位進行維護，需考量其失效程度、成本、風險與使用壽命，排定預防維護階段，最後，利用基因演算法求得每單位成本下最大化的零組件壽命。

  6. 2. Effect of 1P-maintenance to reliability-2.1. Dynamic reliability (1/2) • Ao: initial reliability; A1: degraded factor • Ao與A1可經由模擬或實驗設計取得

  7. 2.1. Dynamic reliability (2/2) • mj-1: the improvement factor of 1P at the (j-1)-th PM stage • t: the effective age of component • tp: the maintenance interval

  8. 2.2. Improvement factor assessment • pij: the probability of 1P activities taken • dij: the improvement level

  9. 3. Maintenance benefit analysis (1/4) • Crj: the CM cost at the j-th stage • C0: the mean cost of per CM action of the system • Cp: the PM cost • Cpi: 1P and 2P cost on subscript p=1 and p=2

  10. 3. Maintenance benefit analysis (2/4) • Tj+1: the extended life of the system on the j-th stage • Bm: the unit-cost life of system • Cs0: the acquired cost of the system • Crn: the CM cost occurred in the last life stage (from tj to Tj+1)

  11. 3. Maintenance benefit analysis (3/4) • Bd: the discarded life of system

  12. 3. Maintenance benefit analysis (4/4)

  13. 4. Genetic algorithms

  14. 5. Case study-5.1. Problem formulation (1/3) • 電子式零組件失效多為隨機發生；機械式零組件失效則為多重損壞所致。

  15. 5.1. Problem formulation (2/3) • Rs(t): the system reliability • RC(t): the reliability of the surplus part of the system except the PM component

  16. 5.1. Problem formulation (3/3)

  17. 5.2. Result analysis (1/4)

  18. 5.2. Result analysis (2/4)

  19. 5.2. Result analysis (3/4)

  20. 5.2. Result analysis (4/4)

  21. 6. Conclusion • 經由量化的計算模式排定1P與2P預防維護階段，可適用於真實的系統。 • 利用動態可靠度(Dynamic reliability)的方程式結合基因演算法排定預防維護計畫，可有效減少計算時間。 • 當零組件的改善因子(improvement factor)愈高，則執行1P的機會將高於2P。

  22. 報告完畢

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