A Bayesian model and numerical algorithm for CBM availability maximization

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 r...

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Vydáno v:Annals of operations research Ročník 196; číslo 1; s. 333 - 348
Hlavní autoři: Jiang, Rui, Kim, Michael Jong, Makis, Viliam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.07.2012
Springer Science + Business Media
Springer
Springer Nature B.V
Témata:
Algorithms
Analysis
Availability
Bayesian analysis
Bayesian statistical decision theory
Business and Management
Combinatorics
Control charts
Decision making
Failure
Linear equations
Linear programming
Markov analysis
Markov processes
Mathematical models
Maximization
Operations research
Operations Research/Decision Theory
Policies
Preventive maintenance
Scheduling (Management)
Studies
Theory of Computation
Canada
Iran
Hidden Markov model
Multivariate Bayesian control chart
Semi-Markov decision process
Availability maximization
Condition-based maintenance
ISSN:0254-5330, 1572-9338
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Shrnutí: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.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-011-1013-1