ArXiv Preprint
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.
Alireza Javanmardi, Eyke Hüllermeier
2022-12-30
