Optimal variability sensitive condition-based maintenance with a Cox PH model

International Journal of Production Research

Volumn 49, Issue 7, 2011, Pages 2083-2100

  Chen, Nan ^a   Chen, Yong ^b   Li, Zhiguo ^c   Zhou, Shiyu ^a   Sievenpiper, Cris ^d  

^a University of Wisconsin Madison   (United States)

^b University of Iowa   (United States)

^c XEROX CORPORATION   (United States)

^d GE HEALTHCARE   (United States)

Author keywords

condition based maintenance; simulation optimisation; variability sensitive decision making

Indexed keywords

CONDITION BASED MAINTENANCE; COST VARIATIONS; FAILURE PROCESS; MAINTENANCE COST; MAINTENANCE DECISION MAKING; MAINTENANCE POLICY; MAINTENANCE STRATEGIES; NESTED PARTITIONS; OPTIMISATIONS; PH MODEL; SENSITIVE CONDITIONS; SEQUENTIAL SELECTION; SIMULATION OPTIMISATION; SYSTEM DEGRADATION; VARIABILITY-SENSITIVE DECISION MAKING;

COMPUTER SIMULATION; DECISION MAKING; DEGRADATION; OPTIMIZATION;

MAINTAINABILITY;

EID: 79551541618     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207541003694811     Document Type: Article

References (29)

1. Baruah, P. and Chinnam, R.B., 2005. HMMs for diagnostics and prognostics in machining processes. International Journal of Production Research, 43 (6), 1275-1293.
2. Bloch-Mercier, S., 2002. A preventive maintenance policy with sequential checking procedure for a Markov deteriorating system. European Journal of Operational Research, 142 (3), 548-576.
3. Chen, C.T., Chen, Y.W., and Yuan, J., 2003. On a dynamic preventive maintenance policy for a system under inspection. Reliability Engineering and System Safety, 80 (1), 41-47.
4. Chen, Y. and Jin, J., 2003. Cost-variability-sensitive preventive maintenance considering management risk. IIE Transactions, 35 (12), 1091-1101.
5. Cox, D.R., 1972. Regression models and life-tables. Journal of the Royal Statistical Society Series B-Statistical Methodology, 34 (2), 187-220.
6. Dieulle, L., et al., 2003. Sequential condition-based maintenance scheduling for a deteriorating system. European Journal of Operational Research, 150 (2), 451-461.
7. Dong, M. and He, D., 2007. Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis. European Journal of Operational Research, 178 (3), 858-878.
8. Grall, A., Berenguer, C., and Dieulle, L., 2002a. A condition-based maintenance policy for stochastically deteriorating systems. Reliability Engineering and System Safety, 76 (2), 167-180.
9. Grall, A., et al., 2002b. Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Transactions on Reliability, 51 (2), 141-150.
10. Hormann, W. and Leydold, J., 2000. Automatic random variate generation for simulation input. In: Proceedings of the 2000 winter simulation conference, vol. 1, 10-13 December, Orlando, Florida, 675-682.
11. Hosseini, M.M., Kerr, R.M., and Randall, R.B., 2000. An inspection model with minimal and major maintenance for a system with deterioration and Poisson failure. IEEE Transactions on Reliability, 49 (1), 88-98.
12. Jardine, A.K.S., Banjevic, D., and Makis, V., 1997. Optimal replacement policy and the structure of software for condition-based maintenance. Journal of Quality in Maintenance Engineering, 3 (2), 109-119.
13. Jardine, A.K.S., Lin, D., and Banjevic, D., 2006. A review on machinary diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20 (7), 1483-1510.
14. Kumar, D. and Westberg, U., 1997. Maintenance scheduling under age replacement policy using proportional hazards model and TTT-ploting. European Journal of Operations Research, 99 (3), 507-515.
15. Leemis, L., Shih, L.H., and Keynertson, K., 1990. Variate generation for accelerated life and proportional hazards models with time dependent cases. Statistics and Probability Letters, 10 (4), 335-339.
16. Leemis, L., 1999. Simulation input modelling. In: Proceedings of the 31st conference on winter simulation: simulation - a bridge to the future. vol. 1, 5-8 December, Phoenix, Arizona, 14-23.
17. Li, Z., et al., 2007. Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures. IIE Transactions, 39 (3), 303-315.
18. Liao, H.T., Elsayed, E.A., and Chan, L.Y., 2006. Maintenance of continuously monitored degrading systems. European Journal of Operational Research, 175 (2), 821-835.
19. Makis, V. and Jardine, A.K.S., 1992. Optimal replacement in the proportional hazards model. INFOR, 30 (1), 172-183.
20. Percy, D.F. and Kobbacy, A.H., 2000. Determining economical maintenance intervals. International Journal of Production Economics, 67 (1), 87-94.
21. Pichitlamken, J., 2002. A combined procedure for optimization via simulation. Dissertation (PhD). Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois.
22. Pichitlamken, J. and Nelson, B., 2003. A combined procedure for optimization via simulation. ACM Transactions on Modeling and Computer Simulation, 13 (2), 155-179.
23. Rangan, A. and Grace, R.E., 1988. A non-Markov model for the optimum replacement of self-repairing systems subject to shocks. Journal of Applied Probability, 25 (2), 375-382.
24. Shi, L. and Chen, C., 2000. A new algorithm for stochastic discrete resource allocation optimization. Discrete Event Dynamic Systems, 10 (3), 271-294.
25. Swisher, J.R. and Jacobson, S.H., 1999. A survey of ranking, selection, and multiple comparison procedures for discrete-event simulation. In: Proceedings of the 31st conference on winter simulation: simulation - a bridge to the future, vol. 1, 5-8 December, Phoenix, Arizona, 492-501.
26. Tapiero, C.S. and Venezia, I., 1979. A mean variance approach to the optimal machine maintenance and replacement. The Journal of the Operational Research Society, 30 (5), 457-466.
27. Valdez-Flores, C. and Feldman, R.M., 1989. A survey of preventive maintenance models for stochastically deteriorating single-unit systems. Naval Research Logistics, 36 (4), 419-446.
28. Wang, H., 2002. A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, 139 (3), 469-489.
29. Wang, W., 2000. A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance. International Journal of Production Research, 38 (6), 1425-1436