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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Vydané v:	Annals of operations research Ročník 196; číslo 1; s. 333 - 348
Hlavní autori:	Jiang, Rui, Kim, Michael Jong, Makis, Viliam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.07.2012 Springer Science + Business Media Springer Springer Nature B.V
Predmet:	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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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.
AbstractList	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. Keywords Availability maximization * Semi-Markov decision process * Hidden Markov model * Condition-based maintenance * Multivariate Bayesian control chart 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. 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.[PUBLICATION ABSTRACT]
Audience	Academic
Author	Jiang, Rui Kim, Michael Jong Makis, Viliam
Author_xml	– sequence: 1 givenname: Rui surname: Jiang fullname: Jiang, Rui organization: Department of Mechanical and Industrial Engineering, University of Toronto – sequence: 2 givenname: Michael Jong surname: Kim fullname: Kim, Michael Jong organization: Department of Mechanical and Industrial Engineering, University of Toronto – sequence: 3 givenname: Viliam surname: Makis fullname: Makis, Viliam email: makis@mie.utoronto.ca organization: Department of Mechanical and Industrial Engineering, University of Toronto
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CitedBy_id	crossref_primary_10_1007_s10479_019_03371_3 crossref_primary_10_1007_s10479_014_1543_4 crossref_primary_10_1016_j_ymssp_2021_107714 crossref_primary_10_1007_s10479_012_1280_5 crossref_primary_10_1016_j_ifacol_2015_06_126 crossref_primary_10_1016_j_ress_2023_109342 crossref_primary_10_1016_j_ress_2023_109694 crossref_primary_10_1016_j_ijpe_2016_01_007 crossref_primary_10_1016_j_apm_2017_09_014
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Keywords	Hidden Markov model Multivariate Bayesian control chart Semi-Markov decision process Availability maximization Condition-based maintenance
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Snippet	In this paper, we consider an availability maximization problem for a partially observable system subject to random failure. System deterioration is described...
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SubjectTerms	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
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Title	A Bayesian model and numerical algorithm for CBM availability maximization
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