Self-Supervised Deep Domain-Adversarial Regression Adaptation for Online Remaining Useful Life Prediction of Rolling Bearing Under Unknown Working Condition

Abstract

This article proposes a novel deep transfer learning-based online remaining useful life (RUL) approach for rolling bearings under unknown working condition. This approach solves the following concerns: the drift of online working condition would block data accumulation and raise bias in the prediction model, and online bearing merely has early fault data when activating RUL prediction, failing to conduct transfer learning from offline data. First, a new transfer learning-based time series recursive forecasting model is constructed to generate online RUL pseudovalues via fusing prior degradation information from offline whole-life data. With such supervised information, a new deep domain-adversarial regression network with multilevel adaptation is further built to transfer prognostic knowledge from offline data to online scenario and evaluate the RUL values of online data batch. Experimental results on the IEEE PHM Challenge 2012 bearing dataset and XJTU-SY bearing dataset validate the effectiveness of the proposed approach.