Multi-wavelet kernel network with distance fusion for bearing cross-domain fault diagnosis

Abstract

In recent years, unsupervised domain adaptation has attracted increasing attention for learning transferable representations from related but distribution-shifted datasets. In bearing fault diagnosis, conventional deep models often exhibit degraded performance under varying operating conditions due to insufficient cross-domain feature alignment. To address this issue, we propose a multiwavelet kernel network with distance fusion domain adaptation (MWKN-DFDA). The model employs a multiwavelet kernel convolution strategy to extract rich temporal and time–frequency-sensitive features from vibration signals, followed by an SE (Squeeze-and-Excitation)-style channel attention module that adaptively recalibrates multiwavelet feature channels and emphasizes fault-relevant responses. On this basis, we design a hierarchical distribution alignment strategy with a three-stage collaborative optimization scheme, integrating sliced Wasserstein distance (SWD) for marginal alignment, conditional SWD for class-conditional alignment with confidence-filtered pseudo-labels, and random Fourier feature-maximum mean discrepancy for nonlinear global alignment in an approximated kernel space. Only source-domain labels are used for training, while target-domain labels are used solely for evaluation. The learned representations are then fed into a classifier and a domain discriminator for joint optimization. Experiments on bearing datasets with varying rotational speeds and noise conditions demonstrate that MWKN-DFDA consistently outperforms competing methods, achieving up to 99.01% accuracy and improved robustness across multiple transfer scenarios.