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A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters

Abstract : With the development of monitoring equipment, research on condition-based maintenance (CBM) is rapidly growing. CBM optimization aims to find an optimal CBM policy which minimizes the average cost of the system over a specified duration of time. This paper proposes a dynamic auto-adaptive predictive maintenance policy for single-unit systems whose gradual deterioration is governed by an increasing stochastic process. The parameters of the degradation process are assumed to be unknown and Bayes' theorem is used to update the prior information. The time interval between two successive inspections is scheduled based on the remaining useful life (RUL) of the system and is updated along with the degradation parameters. A procedure is proposed to dynamically adapt the maintenance decision variables accordingly. Finally, different possible maintenance policies are considered and compared to illustrate their performance.
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Submitted on : Thursday, September 12, 2019 - 3:22:42 PM
Last modification on : Sunday, June 26, 2022 - 4:36:56 AM
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E. Mosayebi Omshi, Antoine Grall, S. Shemehsavar. A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters. European Journal of Operational Research, Elsevier, 2020, 282 (1), ⟨10.1016/j.ejor.2019.08.050⟩. ⟨hal-02285162⟩



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