A Bayesian model and numerical algorithm for CBM availability maximization

Rui Jiang, Michael Kim and Viliam Makis ()

Annals of Operations Research, 2012, vol. 196, issue 1, 333-348

Abstract: In this paper, we consider an availability maximization problem for a partially observable system subject to random failure. System deterioration is described by a hidden, continuous-time homogeneous Markov process. While the system is operational, multivariate observations that are stochastically related to the system state are sampled through condition monitoring at discrete time points. The objective is to design an optimal multivariate Bayesian control chart that maximizes the long-run expected average availability per unit time. We have developed an efficient computational algorithm in the semi-Markov decision process (SMDP) framework and showed that the availability maximization problem is equivalent to solving a parameterized system of linear equations. A numerical example is presented to illustrate the effectiveness of our approach, and a comparison with the traditional age-based replacement policy is also provided. Copyright Springer Science+Business Media, LLC 2012

Keywords: Availability maximization; Semi-Markov decision process; Hidden Markov model; Condition-based maintenance; Multivariate Bayesian control chart (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10479-011-1013-1

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