Journal
IEEE TRANSACTIONS ON RELIABILITY
Volume 71, Issue 2, Pages 1116-1126Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2022.3167046
Keywords
Maintenance engineering; Degradation; Costs; Reliability; Inspection; Optimization; Correlation; Balanced systems; maintenance policy optimization; Markov decision process (MDP); multivariate degradation; stochastic degradation
Categories
Funding
- National Natural Science Foundation of China [72002149, 72032005, 71971181]
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In this article, a novel maintenance policy optimization method is proposed for systems with two balanced components. The method utilizes Markov decision process and dynamic programming algorithms to optimize the maintenance decisions, and examples are provided for illustration and insight.
In this article, we present a novel maintenance policy optimization method for systems with two balanced components. The components in the system are assumed to degrade over time according to a bivariate Wiener process. The maintenance actions aim at eliminating the differences of degradation levels of system components at the cost of aggravating the degradation. Utilizing the Markov decision process, the maintenance model is put forward under both the finite and the infinite planning horizons, from which we find the structural properties of the optimal policies. Backwards dynamic programming and value iteration algorithms are employed to optimize the maintenance decisions. Examples along with sensitivity analysis are presented to facilitate the illustration and insight attainment. We find that the maintenance policies are to a great extent regulated by the absolute degradation difference between the two components.
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