A Gaussian Process Based Method for Data- Efficient Remaining Useful Life Estimation

IEEE Access, vol. 9, pp. 137470–137482

Abstract

The task of remaining useful life (RUL) estimation is a major challenge within the field of prognostics and health management (PHM). The quality of the RUL estimates determines the economical feasibility of the application of predictive maintenance strategies, that rely on accurate predictions. Hence, many effective methods for RUL estimation have been developed in the recent years. Especially deep learning methods have been among the best performing ones setting new record accuracies on bench mark data sets. However, those approaches often rely on numerous and representative run-to-failure sequences of the components under investigation. In real-world use cases, this kind of data (i.e. run-to-failure sequences and RUL labels) is hardly ever present. Therefore, this paper proposes a new, data-efficient method, which is based on Gaussian process classification to derive abstract health indicator (HI) values in a first step, and warped, monotonic Gaussian process regression for indirect RUL estimation in a second step. The proposed approach does neither rely on entire run-to-failure sequences nor on any RUL labels and was tested on the benchmark C-MAPSS turbo fan and FEMTO bearing data sets, achieving comparable results to the state-of-the art whilst using only a small fraction of the available training data. Hence, the proposed approach allows RUL estimation in use cases, in which gathering enough failure data for the application of deep learning models is infeasible.

Authors 3

1. Maximilian Benker corresponding

   Technical University of Munich

   Affiliation as printed

   Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Boltzmannstraße 15, 85748 Garching, Germany. (e-mail: maximilian.benker@iwb.tum.de)

   Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Boltzmannstraße 15, 85748 Garching, Germany

2. Artem Bliznyuk Aachen Institute for Machine Tools and Industrial Management

   RWTH Aachen University · Technical University of Munich

   Affiliation as printed

   Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Boltzmannstraße 15, 85748 Garching, Germany and Institute for Man-Machine Interaction, RWTH Aachen University, Ahornstraße 55, 52074 Aachen, Germany

3. Michael F. Zaeh

   Technical University of Munich

   Affiliation as printed

   Institute for Machine Tools and Industrial Management, Technical University of Munich (TUM), Boltzmannstraße 15, 85748 Garching, Germany