Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method

Abstract

The data distribution discrepancy between the training and test samples makes it challenging for the remaining useful life (RUL) prediction under different working conditions. Although various transfer learning methods focusing on minimizing the distribution discrepancy of global cross-domain features have been applied to address this issue, the inherent properties of each domain are always ignored. The domain private representations caused by it has a negative impact on the RUL prediction of another domain. This paper proposes a novel method called Deep Feature Disentanglement Transfer Learning Network (DFDTLN) to extract domain-invariant features. In the proposed method, shared domain-invariant representations and private representations are disentangled by a pair of joint learning autoencoders. The effectiveness of the proposed method is verified using IEEE PHM Challenge 2012 dataset. The comparison results show the deep features extracted by DFDTLN are more domain-invariant and suitable for RUL prediction.