A residual convolution transfer framework based on slow feature for cross-domain machinery fault diagnosis

Abstract

Intelligent fault diagnosis plays a vital role in ensuring the stable, reliable and safe operation of machinery equipment. However, data distribution doesn’t meet the assumption of the same distribution in the practical scene due to environmental changes. Traditional transfer learning methods can solve the situation of data distribution shift, but they encounter obstacles without adequately considering the possibility of introducing feature pre-extraction into deep learning and fusing pre-extracted low-dimensional feature with high-dimensional feature. In this study, a residual convolution transfer learning framework based on slow feature, which is an invariant or slowly varing signal learned from a vector signal, is presented to address this issue. Firstly, slow feature analysis is employed to implement preliminary feature extraction, which obtains latent variable that reflects the fundamental information of the equipment. Then, slow feature is sent to a residual convolution network for further abstraction and low-dimensional feature aggregates with high-dimensional feature by a bypass branch. Furthermore, maximum mean discrepancy is introduced to calculate the distance between two distributions in the Reproducing Kernel Hilbert Space (RKHS) and reduce the discrepancy of feature distribution. The proposed method is evaluated on Xi’an Jiao Tong University (XJTU) dataset and Case Western Reserve University (CWRU) dataset, and the average accuracy is higher than 99%. Experimental results show that the proposed framework has superior transferability and robustness under different working conditions.