Leaf it to renewal: Improved predictive maintenance policies via renewal theory and decision trees

We propose a general method for deriving prognostics-based predictive maintenance policies. The method takes into account the available decision options at hand, the information on the future state of the system provided by a prognostic model, as well as the costs of the underlying renewal-reward process. It results in heuristic policies with only a few parameters, which can be determined based on theoretical considerations or by optimization from run-to-failure data. We show the potential of the method on two separate predictive maintenance decision settings, namely preventive replacement and preventive ordering. Numerical investigations show that the derived heuristic policies achieve significantly lower cost ratios than other benchmark heuristic policies, while at the same time being more robust against overfitting.