Maintenance optimization of a system subject to two-stage degradation, hard failure, and imperfect repair
•The maintenance optimization of a two-component system is studied.•The degradation of one component is measured with a health indicator.•This health indicator influences the probability of failure of the other component.•The failure probability is found using a proportional hazards model.•A numeric...
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Published in | Reliability engineering & system safety Vol. 237; p. 109313 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.09.2023
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Subjects | |
Online Access | Get full text |
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Summary: | •The maintenance optimization of a two-component system is studied.•The degradation of one component is measured with a health indicator.•This health indicator influences the probability of failure of the other component.•The failure probability is found using a proportional hazards model.•A numerical procedure for finding the long-run cost rate for the system is presented.
This paper develops a condition-based maintenance (CBM) policy for a two-component system subject to continuous degradation and hard failure. The cumulative degradation of one of the components is modeled by a two-stage degradation process, which then determines the failure probability of the other component via a proportional hazards model. Imperfect repair reduces the degradation level without changing the degradation rate, whereas preventive renewal restores both components to the as-good-as-new condition. The CBM policy is optimized by finding the imperfect repair threshold and the preventive renewal time that minimize the long-run cost rate. We propose a numerical procedure to find the solution without Monte Carlo simulation. Furthermore, the modeling framework is flexible enough to account for maintenance delay and opportunistic maintenance. A case study of the maintenance of a cooling system taken from a company in the process industry is presented to illustrate the applicability of the proposed maintenance policy. |
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ISSN: | 0951-8320 1879-0836 |
DOI: | 10.1016/j.ress.2023.109313 |