Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement

•A bayesian updating approach is used to obtain accurate reliability prediction.•A unified maintenance decision framework is proposed.•A two-stage heuristic algorithm is designed to seek optimal maintenance grouping. Prognostic methods for remaining useful life and reliability prediction have been e...

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Published inReliability engineering & system safety Vol. 202; p. 107042
Main Authors Shi, Yue, Zhu, Weihang, Xiang, Yisha, Feng, Qianmei
Format Journal Article
LanguageEnglish
Published Barking Elsevier Ltd 01.10.2020
Elsevier BV
Subjects
Algorithms
Bayesian analysis
Complex systems
Component reliability
Condition-based maintenance
Decision making
Degradation
Dynamic environment
Environmental information
Heuristic methods
Inspection
Multi-component systems
Optimization
Predictive maintenance
Predictive reliability
Preventive maintenance
Reliability aspects
Reliability engineering
System reliability
Predictive reliability
Multi-component systems
Condition-based maintenance
Dynamic environment
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Abstract •A bayesian updating approach is used to obtain accurate reliability prediction.•A unified maintenance decision framework is proposed.•A two-stage heuristic algorithm is designed to seek optimal maintenance grouping. Prognostic methods for remaining useful life and reliability prediction have been extensively studied in the past decade. However, the use of prognostic information and methods in maintenance decision-making for complex systems is still an underexplored area. In this paper, using a rolling-horizon approach, we develop a condition-based maintenance decision-framework for a multi-component system subject to a system reliability requirement. The system is inspected periodically and new degradation information on components is obtained upon inspection. The new degradation observations are used to update the posterior distributions of the failure model parameters via Bayesian updating, providing more accurate and customized predictive reliabilities. If the predictive system reliability is below the reliability requirement, a novel dynamic-priority-based heuristic algorithm is used to identify a group of components for preventive maintenance. Numerical results show that significant cost savings and improved system reliabilities can be obtained by using more accurate predictive information in maintenance decision-making. We further illustrate the modeling flexibility of the proposed framework by considering dynamic environmental information in decision-making and investigate the potential benefits of incorporating dynamic contexts.
AbstractList Prognostic methods for remaining useful life and reliability prediction have been extensively studied in the past decade. However, the use of prognostic information and methods in maintenance decision-making for complex systems is still an underexplored area. In this paper, using a rolling-horizon approach, we develop a condition-based maintenance decision-framework for a multi-component system subject to a system reliability requirement. The system is inspected periodically and new degradation information on components is obtained upon inspection. The new degradation observations are used to update the posterior distributions of the failure model parameters via Bayesian updating, providing more accurate and customized predictive reliabilities. If the predictive system reliability is below the reliability requirement, a novel dynamic-priority-based heuristic algorithm is used to identify a group of components for preventive maintenance. Numerical results show that significant cost savings and improved system reliabilities can be obtained by using more accurate predictive information in maintenance decision-making. We further illustrate the modeling flexibility of the proposed framework by considering dynamic environmental information in decision-making and investigate the potential benefits of incorporating dynamic contexts.
•A bayesian updating approach is used to obtain accurate reliability prediction.•A unified maintenance decision framework is proposed.•A two-stage heuristic algorithm is designed to seek optimal maintenance grouping. Prognostic methods for remaining useful life and reliability prediction have been extensively studied in the past decade. However, the use of prognostic information and methods in maintenance decision-making for complex systems is still an underexplored area. In this paper, using a rolling-horizon approach, we develop a condition-based maintenance decision-framework for a multi-component system subject to a system reliability requirement. The system is inspected periodically and new degradation information on components is obtained upon inspection. The new degradation observations are used to update the posterior distributions of the failure model parameters via Bayesian updating, providing more accurate and customized predictive reliabilities. If the predictive system reliability is below the reliability requirement, a novel dynamic-priority-based heuristic algorithm is used to identify a group of components for preventive maintenance. Numerical results show that significant cost savings and improved system reliabilities can be obtained by using more accurate predictive information in maintenance decision-making. We further illustrate the modeling flexibility of the proposed framework by considering dynamic environmental information in decision-making and investigate the potential benefits of incorporating dynamic contexts.
ArticleNumber 107042
Author Xiang, Yisha
Shi, Yue
Zhu, Weihang
Feng, Qianmei
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  givenname: Weihang
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  givenname: Yisha
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  givenname: Qianmei
  surname: Feng
  fullname: Feng, Qianmei
  email: qfeng@central.uh.edu
  organization: Department of Industrial Engineering, University of Houston, Houston, TX, 77204, USA
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Condition-based maintenance
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  doi: 10.1016/j.ejor.2005.05.017
– volume: 226
  start-page: 53
  issue: 1
  year: 2013
  ident: 10.1016/j.ress.2020.107042_bib0027
  article-title: A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution
  publication-title: European Journal of Operational Research
  doi: 10.1016/j.ejor.2012.10.030
SSID ssj0004957
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Snippet •A bayesian updating approach is used to obtain accurate reliability prediction.•A unified maintenance decision framework is proposed.•A two-stage heuristic...
Prognostic methods for remaining useful life and reliability prediction have been extensively studied in the past decade. However, the use of prognostic...
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SubjectTerms Algorithms
Bayesian analysis
Complex systems
Component reliability
Condition-based maintenance
Decision making
Degradation
Dynamic environment
Environmental information
Heuristic methods
Inspection
Multi-component systems
Optimization
Predictive maintenance
Predictive reliability
Preventive maintenance
Reliability aspects
Reliability engineering
System reliability
Title Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement
URI https://dx.doi.org/10.1016/j.ress.2020.107042
https://www.proquest.com/docview/2505722488
Volume 202
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