臺灣博碩士論文加值系統

系統會隨著使用年限和使用而老化。為了維護系統狀況並防止突然故障，預防性維護 (Preventive Maintenance, PM) 策略是迄今為止可以應用的最佳方法。計劃性的PM操作可以提高系統可用性並最大限度地減少因故障和失效造成的損失。最近，條件基礎PM (Condition-based Preventive Maintenance, CBPM) 策略已經成為一個被廣泛討論的主題，因為它被認為比年齡基礎PM (Age-based Preventive Maintenance, ABPM) 策略更可使用於老化可修復系統。本研究將產品良率視為決定最佳PM計劃的條件變量，針對其對應於每單位時間的最小預期維護和懲罰成本，求出有最佳良率門檻值。非齊次卜瓦松過程 (Non-homogeneous Poisson Process, NHPP) 用於描述系統的老化過程，系統有效年齡的概念也能使模型構建更具效能。本研究提出了三種條件基礎PM策略，提供決策者更多樣化的選擇，期能根據不同情境使用不同模式以解決問題。數值應用的結果指出，高良率門檻值可以最小化系統故障情形，同時提高系統可用性和產品質量，即使它可能由於頻繁的PM動作而導致高成本。另一方面，透過敏感性分析可發現預期的成本和懲罰成本參數之變動對維護與懲罰成本的影響十分顯著。當兩個成本參數都很低時，採取所提出的PM策略會更有效，而當兩個成本參數都很高時，此時所有策略成本往往很高。

A system will deteriorate with age and usage. In order to maintain system conditions and prevent sudden failures, preventive maintenance (PM) policy still be the best method that can be applied to date. The scheduled PM actions can improve system availability and minimize losses due to breakdowns and failures. Lately, the condition-based PM policy has become a widely discussed topic because it is considered more relevant than the age-based PM policy for deteriorating repairable systems. In this study, we consider the product yield rate as the condition variable in determining the optimal PM schedule. The PM model is constructed with the optimal yield rate threshold which corresponds to the minimum expected maintenance and penalty cost per unit time. A non-homogeneous Poisson process is used to describe the system deterioration and the concept of system effective age is also considered to make the model more realistic. Three condition-based PM strategies are presented to provide more diverse choices for decision makers in solving problems according to the situation at hand. The results of numerical applications show that a high yield rate threshold can minimizes system failure while increases system availability and product quality even though it may result in high costs due to frequent PM actions. On the other hand, the sensitivity analysis show that the expected maintenance and penalty cost is sensitive to the PM and penalty cost parameter. The proposed PM strategies are more efficient to be performed when both cost parameters are low, while they tend to be costly when both cost parameters are high.

Contents

摘要 I
Abstract II
Acknowledgements III
Contents…………………………………………………………..………………………..IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Objective 3
1.4 Scope and Importance 4
1.5 Organization 5
Chapter 2 Literature Review 6
2.1 System Deterioration Process 6
2.2 Preventive Maintenance Policies 9
2.2.1 Periodic and Non-Periodic PM Policy 11
2.2.2 Time-based PM Policy 12
2.2.3 Condition-based PM Policy 14
2.3 Yield Rate and CBPM 17
Chapter 3 Condition-based PM Model 21
3.1 Problem Description 21
3.2 Research Framework 25
3.3 Model Construction 28
3.3.1 PM Strategy 1 (The optimal number of PM actions) 35
3.3.2 PM Strategy 2 (The optimal yield rate threshold that corresponds to the optimal number of PM actions) 38
3.3.3 PM Strategy 3 (The optimal yield rate thresholds for each PM period that corresponds to the optimal number of PM actions) 41
Chapter 4 Numerical Applications 45
4.1 Case Description 45
4.2 Sensitivity Analysis 51
Chapter 5 Conclusion 57
5.1 Contribution 57
5.2 Research Limitations 59
5.3 Future Research 60
Bibliography 61

Ahmad, R., ＆ Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers ＆ Industrial Engineering, 63(1), 135-149.
Alaswad, S., ＆ Xiang, Y. (2017). A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering ＆ System Safety, 157, 54-63.
Baik, J., Murthy, D. N. P., ＆ Jack, N. (2004). Two‐dimensional failure modeling with minimal repair. Naval Research Logistics (NRL), 51(3), 345-362.
Banjevic, D., Jardine, A. K. S., Makis, V., ＆ Ennis, M. (2001). A control-limit policy and software for condition-based maintenance optimization. INFOR: Information Systems and Operational Research, 39(1), 32-50.
Bouslah, B., Gharbi, A., ＆ Pellerin, R. (2016). Joint economic design of production, continuous sampling inspection and preventive maintenance of a deteriorating production system. International Journal of Production Economics, 173, 184-198.
Bouslah, B., Gharbi, A., ＆ Pellerin, R. (2018). Joint production, quality and maintenance control of a two-machine line subject to operation-dependent and quality-dependent failures. International Journal of Production Economics, 195, 210-226.
Caballé, N. C., Castro, I. T., Pérez, C. J., ＆ Lanza-Gutiérrez, J. M. (2015). A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliability Engineering ＆ System Safety, 134, 98-109.
Castanier, B., Grall, A., ＆ Bérenguer, C. (2005). A condition-based maintenance policy with non-periodic inspections for a two-unit series system. Reliability Engineering ＆ System Safety, 87(1), 109-120.
Cheng, G. Q., Zhou, B. H., ＆ Li, L. (2018). Integrated production, quality control and condition-based maintenance for imperfect production systems. Reliability Engineering ＆ System Safety, 175, 251-264.
de Jonge, B., Teunter, R., ＆ Tinga, T. (2017). The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance. Reliability engineering ＆ system safety, 158, 21-30.
Deloux, E., Castanier, B., ＆ Bérenguer, C. (2009). Predictive maintenance policy for a gradually deteriorating system subject to stress. Reliability Engineering ＆ System Safety, 94(2), 418-431.
Doyen, L., ＆ Gaudoin, O. (2004). Classes of imperfect repair models based on reduction of failure intensity or virtual age. Reliability Engineering ＆ System Safety, 84(1), 45-56.
Hajipour, Y., ＆ Taghipour, S. (2016). Non-periodic inspection optimization of multi-component and k-out-of-m systems. Reliability Engineering ＆ System Safety, 156, 228-243.
Huang, Y. S. (2001). A decision model for deteriorating repairable systems. IIE Transactions, 33(6), 479-485.
Huang, Y. S., ＆ Yen, C. (2009). A study of two-dimensional warranty policies with preventive maintenance. IIE Transactions, 41(4), 299-308.
Jiang, S. T., Landers, T. L., ＆ Rhoads, T. R. (2006). Assessment of repairable-system reliability using proportional intensity models: A review. IEEE Transactions on Reliability, 55(2), 328-336.
Kazaz, B., ＆ Sloan, T. W. (2013). The impact of process deterioration on production and maintenance policies. European Journal of Operational Research, 227(1), 88-100.
Kim, J., ＆ Gershwin, S. B. (2008). Analysis of long flow lines with quality and operational failures. IIE Transactions, 40(3), 284-296.
Krivtsov, V. V. (2007). Practical extensions to NHPP application in repairable system reliability analysis. Reliability Engineering ＆ System Safety, 92(5), 560-562.
Lam, Y. (2007). A geometric process maintenance model with preventive repair. European Journal of Operational Research, 182(2), 806-819.
Lin, Z. L., Huang, Y. S., ＆ Fang, C. C. (2015). Non-periodic preventive maintenance with reliability thresholds for complex repairable systems. Reliability Engineering ＆ System Safety, 136, 145-156.
Mehdi, R., Nidhal, R., ＆ Anis, C. (2010). Integrated maintenance and control policy based on quality control. Computers ＆ Industrial Engineering, 58(3), 443-451.
Muralidharan, K. (2002). Reliability inferences of modulated power-law process# i. IEEE Transactions on Reliability, 51(1), 23-26.
Nodem, F. D., Kenné, J. P., ＆ Gharbi, A. (2011). Simultaneous control of production, repair/overhaul and preventive maintenance of deteriorating manufacturing systems. International Journal of Production Economics, 134(1), 271-282.
Poppe, J., Basten, R. J., Boute, R. N., ＆ Lambrecht, M. R. (2017). Numerical study of inventory management under various maintenance policies. Reliability Engineering ＆ System Safety, 168, 262-273.
Poppe, J., Boute, R. N., ＆ Lambrecht, M. R. (2018). A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds. European Journal of Operational Research, 268(2), 515-532.
Rivera-Gomez, H., Gharbi, A., ＆ Kenné, J. P. (2013). Joint control of production, overhaul, and preventive maintenance for a production system subject to quality and reliability deteriorations. The International Journal of Advanced Manufacturing Technology, 69(9-12), 2111-2130.
Rivera-Gómez, H., Gharbi, A., ＆ Kenné, J. P. (2013). Joint production and major maintenance planning policy of a manufacturing system with deteriorating quality. International Journal of Production Economics, 146(2), 575-587.
Rivera-Gómez, H., Gharbi, A., Kenné, J. P., Montaño-Arango, O., ＆ Hernández-Gress, E. S. (2018). Subcontracting strategies with production and maintenance policies for a manufacturing system subject to progressive deterioration. International Journal of Production Economics, 200, 103-118.
Saghaei, A., Najafi, H., ＆ Noorossana, R. (2012). Enhanced rolled throughput yield: A new six sigma-based performance measure. International Journal of Production Economics, 140(1), 368-373.
Selim, M., ＆ Gurel, A. S. (2007). Machining conditions-based preventive maintenance. International Journal of Production Research, 45(8), 1725-1743.
Sheu, S. H., Liu, T. H., Zhang, Z. G., ＆ Tsai, H. N. (2018). The generalized age maintenance policies with random working times. Reliability Engineering ＆ System Safety, 169, 503-514.
Shin, J. H., ＆ Jun, H. B. (2015). On condition based maintenance policy. Journal of Computational Design and Engineering, 2(2), 119-127.
Sloan, T. (2008). Simultaneous determination of production and maintenance schedules using in‐line equipment condition and yield information. Naval Research Logistics (NRL), 55(2), 116-129.
Sloan, T. W., ＆ Shanthikumar, J. G. (2002). Using in-line equipment condition and yield information for maintenance scheduling and dispatching in semiconductor wafer fabs. IIE Transactions, 34(2), 191-209.
Van, P. D., ＆ Bérenguer, C. (2012). Condition‐based maintenance with imperfect preventive repairs for a deteriorating production system. Quality and Reliability Engineering International, 28(6), 624-633.
Wang, H. (2002). A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139(3), 469-489.
Wang, W. (2002). A model to predict the residual life of rolling element bearings given monitored condition information to date. IMA Journal of Management Mathematics, 13(1), 3-16.
Weckman, G. R., Shell, R. L., ＆ Marvel, J. H. (2001). Modeling the reliability of repairable systems in the aviation industry. Computers ＆ industrial engineering, 40(1-2), 51-63.
Wu, S., ＆ Clements-Croome, D. (2006). A novel repair model for imperfect maintenance. IMA Journal of Management Mathematics, 17(3), 235-243.
Xiang, Y., Cassady, C. R., Jin, T., ＆ Zhang, C. W. (2014). Joint production and maintenance planning with machine deterioration and random yield. International Journal of Production Research, 52(6), 1644-1657.
Yang, L., Zhao, Y., ＆ Ma, X. (2018). Multi-level maintenance strategy of deteriorating systems subject to two-stage inspection. Computers ＆ Industrial Engineering, 118, 160-169.
Yu, J. W., Tian, G. L., ＆ Tang, M. L. (2007). Predictive analyses for nonhomogeneous Poisson processes with power law using Bayesian approach. Computational Statistics ＆ Data Analysis, 51(9), 4254-4268.
Zhou, X., Xi, L., ＆ Lee, J. (2007). Reliability-centered predictive maintenance scheduling for a continuously monitored system subject to degradation. Reliability Engineering ＆ System Safety, 92(4), 530-534.