Degradation Alignment in Remaining Useful Life Prediction Using Deep Cycle-Consistent Learning

Due to the benefits of reduced maintenance cost and increased operational safety, effective prognostic methods have always been highly demanded in real industries. In the recent years, intelligent data-driven remaining useful life (RUL) prediction approaches have been successfully developed and achieved promising performance. However, the existing methods mostly set hard RUL labels on the training data and pay less attention to the degradation pattern variations of different entities. This article proposes a deep learning-based RUL prediction method. The cycle-consistent learning scheme is proposed to achieve a new representation space, where the data of different entities in similar degradation levels can be well aligned. A first predicting time determination approach is further proposed, which facilitates the following degradation percentage estimation and RUL prediction tasks. The experimental results on a popular degradation data set suggest that the proposed method offers a novel perspective on data-driven prognostic studies and a promising tool for RUL estimations.

Automated summary

This paper proposes a deep learning-based RUL prediction method, which aligns the data of different entities in similar degradation levels through a cycle-consistent learning scheme to improve prediction performance. Experimental results suggest that the method offers a novel perspective on RUL estimations.