A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance

TL;DR

This paper introduces a new metric to evaluate and optimize data-driven prognostic algorithms based on their impact on downstream predictive maintenance decisions, aiming to improve maintenance cost efficiency.

Contribution

It proposes a decision-oriented metric linked to PdM policies, and demonstrates its use for optimizing prognostic algorithms and policies in predictive maintenance.

Findings

The metric effectively assesses prognostic algorithms' impact on maintenance costs.

Optimizing algorithms with the metric improves decision-making in predictive maintenance.

The approach is validated on simulated turbofan engine data.

Abstract

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. We here 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.