Deep temporal–spectral domain adaptation for bearing fault diagnosis

Abstract

Deep transfer learning (DTL) greatly improved the cross-domain generalization of fault diagnosis and makes it more practical and operable. However, existing work focuses on addressing temporal feature shift, while neglecting the modeling and narrow of spectral feature shift. To solve this issue, this work focus on the study of temporal–spectral domain adaption (TSDA) for bearing fault diagnosis and proposes a temporal–spectral domain adaptive network (TSDAN). Specifically, TSDAN constructs a temporal–spectral representation by extracting temporal features and spectral features through two branching modules: a convolutional network and a novel spectral neural network, respectively. To construct spectral neural networks, we introduce spectral convolution, spectral pooling, spectral normalization, and spectral activation. Moreover, a Sinkhorn divergence-based temporal-spectrum adapter is designed to align the temporal-spectrum representations from the source and target domains. Finally, we provide the implementation details of TSDAN-based fault diagnosis on publicly available and self-built datasets, which validate the effectiveness and superiority of the proposed approach.