Selecting the appropriate Predictive Maintenance algorithm
new paper out by Antonios Kamariotis in Volume 242 of Reliability Engineering & System Safety
Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. In this work, led by Antonis Kamariotis (TUM, ETHZ), we propose a metric for assessing data-driven prognostic algorithms based on their impact on downstream PdM decisions.
The metric is defined in association with a decision setting and a corresponding PdM policy. We consider two typical PdM decision settings, namely component ordering and/or replacement planning, for which we investigate and improve PdM policies that are commonly utilized in the literature. All policies are evaluated via the data-based estimation of the long-run expected maintenance cost per unit time, using monitored run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. We employ the metric as an objective function for optimizing heuristic PdM policies and algorithms’ hyperparameters.
The effect of different PdM policies on the metric is initially investigated through a theoretical numerical example. Subsequently, we employ four data-driven prognostic algorithms on a simulated turbofan engine degradation problem, and investigate the joint effect of prognostic algorithm and PdM policy on the metric, resulting in a decision-oriented performance assessment of these algorithms.
This work forms a collaboration between Daniel Straub and Antonis Kamariotis of the Engineering Risk Analysis Group at the Technical University of Munich, Kai Goebel of the Palo Alto Research Center (PARC), and Konstantinos Tatsis and Eleni Chatzi of our Chair of Structural Mechanics and Monitoring at ETH Zurich. The work has been conducted under a Hans Fischer Fellowship offered by the Institute for Advanced Study at TUM to Prof. Eleni Chatzi and is supported by the TÜV-SÜD Foundation.
Read more on Volume 204 of external page Reliability Engineering & System Safety