Optimal task-driven time-dependent covariate-based maintenance policy. (English) Zbl 1528.90094
Summary: In this paper, a multi-component series system is considered. The system is monitored periodically. The exact cause of failure is assumed to be masked or missed. In the masked setup, the exact cause of failure is unknown but the set to which it belongs, called the masked set, is known. While in the missing setup, there is no information about the exact cause of failure. A time-dependent covariate-based maintenance policy is proposed such that the maintenance action and cost of the failed components at inspection times depend on several factors and can vary. The component lifetime distributions are considered unknown. The proposed maintenance policy is optimized using some task-driven decision-making statistical learning methods. Finally, the applicability of the proposed theory is analyzed through some numerical analysis. The results are compared to the case where lifetime distributions are known as a benchmark.
MSC:
| 90B25 | Reliability, availability, maintenance, inspection in operations research |
| 60K10 | Applications of renewal theory (reliability, demand theory, etc.) |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
Keywords:
perfect corrective maintenance; minimal corrective maintenance; imperfect corrective maintenance; task-driven decision-making; masked data; statistical and machine learning algorithmsReferences:
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