Bi-level corrected residual life-based maintenance for deteriorating systems under competing risks
•Competing risks model with aging and continuous-state degradation.•Development of prognostic approach with dynamic corrective mechanisms.•Development of corrected residual life-based maintenance policy with dynamic control policy.•Development of SMDP approach to optimize the multi-objective mainten...
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| Vydáno v: | Reliability engineering & system safety Ročník 247; s. 110069 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.07.2024
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| ISSN: | 0951-8320, 1879-0836 |
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| Abstract | •Competing risks model with aging and continuous-state degradation.•Development of prognostic approach with dynamic corrective mechanisms.•Development of corrected residual life-based maintenance policy with dynamic control policy.•Development of SMDP approach to optimize the multi-objective maintenance model.•Considerably better MRL and maintenance performance compared with published results.
The accurate prediction of residual life in deteriorating systems is a challenging task due to uncertainties and idealized assumptions during data acquisition and model construction. To address this challenge, the paper proposes a bi-level maintenance model that incorporates a corrected residual life (CRL) approach for a deteriorating system under competing risks. Specifically, the degradation failure is modeled using a Gamma process, while the competing sudden failure is described by a proportional hazards (PH) model. To reduce inconsistencies in residual life prediction, a CRL model is presented and continuously updated with a dynamic corrective factor at each monitoring epoch. Based on the updated CRL, a bi-level maintenance model is developed, which considers both system availability and maintenance cost objectives, enabling dynamic monitoring of the system's degradation state and residual life. The optimal decision variables in the maintenance model are determined through a multi-objective optimization algorithm formulated within a semi-Markov decision process (SMDP) framework. The unique aspect of this work lies in the consideration of prediction errors in residual life and the incorporation of multi-attribute optimization for maintenance design. The proposed method is validated through a case study on light-emitting diodes, confirming its effectiveness in improving residual life prediction and optimizing maintenance decisions. |
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| AbstractList | •Competing risks model with aging and continuous-state degradation.•Development of prognostic approach with dynamic corrective mechanisms.•Development of corrected residual life-based maintenance policy with dynamic control policy.•Development of SMDP approach to optimize the multi-objective maintenance model.•Considerably better MRL and maintenance performance compared with published results.
The accurate prediction of residual life in deteriorating systems is a challenging task due to uncertainties and idealized assumptions during data acquisition and model construction. To address this challenge, the paper proposes a bi-level maintenance model that incorporates a corrected residual life (CRL) approach for a deteriorating system under competing risks. Specifically, the degradation failure is modeled using a Gamma process, while the competing sudden failure is described by a proportional hazards (PH) model. To reduce inconsistencies in residual life prediction, a CRL model is presented and continuously updated with a dynamic corrective factor at each monitoring epoch. Based on the updated CRL, a bi-level maintenance model is developed, which considers both system availability and maintenance cost objectives, enabling dynamic monitoring of the system's degradation state and residual life. The optimal decision variables in the maintenance model are determined through a multi-objective optimization algorithm formulated within a semi-Markov decision process (SMDP) framework. The unique aspect of this work lies in the consideration of prediction errors in residual life and the incorporation of multi-attribute optimization for maintenance design. The proposed method is validated through a case study on light-emitting diodes, confirming its effectiveness in improving residual life prediction and optimizing maintenance decisions. |
| ArticleNumber | 110069 |
| Author | Li, Xinmin Duan, Chaoqun Yan, Liangwen Gong, Ting |
| Author_xml | – sequence: 1 givenname: Chaoqun orcidid: 0000-0002-8938-5474 surname: Duan fullname: Duan, Chaoqun email: chaoqun.duan@hotmail.com organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 2 givenname: Ting surname: Gong fullname: Gong, Ting email: tingong@ucdavis.edu organization: University of California, Davis, Davis, CA 95616, USA – sequence: 3 givenname: Liangwen surname: Yan fullname: Yan, Liangwen email: lw_yan@shu.edu.cn organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China – sequence: 4 givenname: Xinmin orcidid: 0000-0001-7923-2792 surname: Li fullname: Li, Xinmin email: xinmin@kth.se organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China |
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| Keywords | Condition monitoring Maintenance model Competing risks Prognostics and health management |
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