Self-Supervised Deep Tensor Domain-Adversarial Regression Adaptation for Online Remaining Useful Life Prediction Across Machines

Abstract

With deep transfer learning techniques, this paper focuses on the online remaining useful life (RUL) prediction problem across different machines, and tries to address the following concerns: 1) The effect of transfer learning decreases significantly due to considerable divergence of degradation characteristic; 2) A high computational cost is raised by re-training whole model with online data; 3) Error accumulation occurs because of lacking label information of online data. In this paper, a self-supervised deep tensor domain-adversarial regression adaptation approach is proposed. In the pre-training stage, a novel tensor domain-adversarial network, with a tensorized domain discriminator, is constructed using the offline whole-life degradation data and early fault data of the target machine. A new training algorithm with an alternating minimization scheme is then built to seek the optimal core tensor and domain-invariant feature representation. In the online stage, a new self-supervised fine-tuning strategy is designed for the target network initialized from the pre-trained network. The core tensor-formed self-supervised information, extracted from the monotonicity of online degradation process, and the pseudo-supervised information from the pre-trained network are integrated to realize fast and adaptive RUL prediction. This paper takes rolling bearing as an example, and runs both cross-conditions and cross-machines experiments on three rolling bearings datasets, i.e., IEEE PHM Challenge 2012, XJTU-SY and our test rig. The results verify the use of tensor representation can facilitate regression adversarial training, and demonstrate the proposed approach can effectively improve predictive accuracy and stability under unknown online conditions.