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A data-driven approach for condition-based maintenance optimization
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DOI：10.1016/j.ejor.2023.05.002.png)
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Abstract
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Developments in sensor techniques enable the continuous monitoring of the health of an operating sys-tem. The resulting condition data provides an opportunity for better prediction of failures and thereby for improving maintenance decisions. In this study, we consider condition-based maintenance for a single unit with an unknown, non-decreasing deterioration process and unknown failure behavior. Building on, but different from the existing maintenance optimization literature, we present the first fully data-driven approach, where the condition threshold triggering maintenance is based purely on past condition data and failures. Numerical results for a gamma deterioration process show that the maintenance threshold resulting from our data-driven approach converges to the optimal threshold. The threshold is set higher during the initial runs-to-failure, and this helps to explore the deterioration process. An encouraging re-sult is that the convergence is especially fast during the first few runs-to-failure so that the expected cost rate quickly converges to the minimum cost rate.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Keywords:
Reliability
Condition-based maintenance
Data-driven
Deterioration process
Control limit policy
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