A predictive maintenance model for k-out-of-n:F continuously deteriorating systems subject to stochastic and economic dependencies

K.T. Huynh, H.C. Vu, T.D. Nguyen and A.C. Ho

Reliability Engineering and System Safety, 2022, vol. 226, issue C

Abstract: New concepts of Industry 4.0 make industrial systems more and more complex due to the strong dependencies among a large number of stochastically deteriorating units. Ignoring such dependencies in maintenance modeling usually leads to high maintenance costs. With this spirit, we develop in the present paper an innovative predictive maintenance model for k-out-of-n:F continuously deteriorating systems considering both the stochastic dependency due to the common impact of working environment and the economic dependency issued by the set-up cost sharing. Compared with related works, the originality of our model is fourfold. Firstly, we adopt a multivariate Gamma subordinator to describe the dependent degradation behavior among units in the context of maintenance modeling. Secondly, we use probabilistic measures of the remaining useful life at unit and system levels as maintenance decision indices. Thirdly, we elaborate a proactive maintenance policy allowing the consideration of both the stochastic and economic dependencies in maintenance decision-making. Finally, we evaluate analytically the long-run cost rate of the maintenance policy thanks to the semi-regenerative theory. Various comparative studies with benchmarks under different configurations of system characteristics and maintenance costs confirm the high performances of the developed maintenance model, and emphasize the value of introducing multiple kinds of dependencies in maintenance modeling.

Keywords: Cost model; Economic and stochastic dependencies; Multivariate Gamma subordinator; Predictive maintenance model; Remaining useful life; Semi-regenerative process (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003052

DOI: 10.1016/j.ress.2022.108671

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