Nonparametric adaptive opportunity-based replacement strategies

Coolen-Schrijner, P.; Coolen, F.P.A.; Shaw, S.C.

Abstract

We consider opportunity-based age replacement (OAR) using nonparametric predictive inference (NPI) for the time to failure of a future unit. Based on n observed failure times, NPI provides lower and upper bounds for the survival function for the time to failure Xn+1 of a future unit which lead to upper and lower cost functions, respectively, for OAR based on the renewal reward theorem. Optimal OAR strategies for unit n+1 follow by minimizing these cost functions. Following this strategy, unit n+1 is correctively replaced upon failure, or preventively replaced upon the first opportunity after the optimal OAR threshold. We study the effect of this replacement information for unit n+1 on the optimal OAR strategy for unit n+2. We illustrate our method with examples and a simulation study. Our method is fully adaptive to available data, providing an alternative to the classical approach where the probability distribution of a unit's time to failure is assumed to be known. We discuss the possible use of our method and compare it with the classical approach, where we conclude that in most situations our adaptive method performs very well, but that counter-intuitive results can occur.

Citation

Coolen-Schrijner, P., Coolen, F., & Shaw, S. (2006). Nonparametric adaptive opportunity-based replacement strategies. Journal of the Operational Research Society, 57(1), 63-81. https://doi.org/10.1057/palgrave.jors.2601954

• Journal Article Type: Article
• Publication Date: 2006-01
• Deposit Date: Nov 18, 2008
• Journal: Journal of the Operational Research Society
• Print ISSN: 0160-5682
• Electronic ISSN: 1476-9360
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 57
• Issue: 1
• Pages: 63-81
• DOI: https://doi.org/10.1057/palgrave.jors.2601954
• Keywords: Opportunity-based age replacement, Nonparametric predictive inference, Renewal reward theorem.
• Public URL: https://durham-repository.worktribe.com/output/1567256