Abstract:We propose a hybrid planning method for deriving prognostics-based predictive maintenance policies. The method accounts for the available decision options, the information on the future state of the system provided by a prognostic model, and the costs of the underlying renewal-reward process. It results in policies defined by only a few parameters, which can be determined based on theoretical considerations or by optimization from run-to-failure data. We demonstrate the potential of the method in two separate predictive maintenance decision settings: preventive replacement and preventive ordering. Numerical investigations show that the derived policies rival the performance of optimized benchmark policies, while being significantly more efficient and robust against overfitting.
From: Daniel Koutas [view email]
[v1]
Wed, 24 Sep 2025 14:09:55 UTC (6,589 KB)
[v2]
Thu, 9 Oct 2025 08:30:28 UTC (6,589 KB)
[v3]
Fri, 12 Jun 2026 16:58:05 UTC (5,238 KB)
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