e-RULENet: remaining useful life estimation with end-to-end learning from long run-to-failure data

Abstract

This paper presents the e-RULENet, which is a novel framework to train a data-driven model for remaining useful life estimation from long run-to-failure data with an end-to-end manner. In order to enable end-to-end learning, a change point from the healthy stage to the degradation stage is estimated for each instance from measurements. The change point estimation in the previous framework is computationally expensive since it utilizes entire measurements over the lifetime for each instance. The e-RULENet solves this issue with estimating the change points with segments sampled from run-to-failure data. It is evaluated on three datasets for RUL estimation including two real-world datasets, and on a dataset for tension estimation as another use case. It outperforms an approach without end-to-end learning on all the three datasets. The results indicates that the e-RULENet works not only for cases with multiple health stages but also for a single degradation stage, which does not have any changes points.

Keywords

• remaining useful life estimation
• predictive maintenance
• deep learning
• sequential model
• end-to-end learning