Conformal Prediction Intervals for Remaining Useful Lifetime Estimation

TL;DR

This paper applies conformal prediction methods to quantify uncertainty in Remaining Useful Lifetime estimates, transforming point predictions into reliable interval predictions with formal coverage guarantees.

Contribution

It introduces three conformal prediction algorithms for RUL estimation and demonstrates their effectiveness on aerospace system data using deep learning and gradient boosting models.

Findings

Conformal prediction provides valid RUL intervals with guaranteed coverage.

Deep CNN and gradient boosting models can be effectively conformalized for RUL.

The approach improves uncertainty quantification in prognostics applications.

Abstract

The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in working order before starting to function incorrectly. In recent years, numerous machine learning algorithms have been proposed for RUL estimation, mainly focusing on providing more accurate RUL predictions. However, there are many sources of uncertainty in the problem, such as inherent randomness of systems failure, lack of knowledge regarding their future states, and inaccuracy of the underlying predictive models, making it infeasible to predict the RULs precisely. Hence, it is of utmost importance to quantify the uncertainty alongside the RUL predictions. In this work, we investigate the conformal prediction (CP) framework that represents uncertainty by predicting sets of possible values for the target variable (intervals in the case of RUL) instead of making point predictions. Under very mild technical assumptions, CP formally guarantees that the actual value (true RUL) is covered by the predicted set with a degree of certainty that can be prespecified. We study three CP algorithms to conformalize any single-point RUL predictor and turn it into a valid interval predictor. Finally, we conformalize two single-point RUL predictors, deep convolutional neural networks and gradient boosting, and illustrate their performance on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data sets.