DWAN: a dictionary-wavelet attention network for noise-robust bearing fault diagnosis

Abstract

Reliable operation of bearings in complex industrial environments is essential. To mitigate the adverse effect of noise interference on fault-pattern recognition in bearing fault diagnosis, this paper proposes a new denoising diagnostic framework, termed the dictionary-wavelet attention network. The framework integrates a dictionary-based denoising autoencoder (DDAE) and a wavelet time–frequency attention (WTFA) module. In the DDAE, the decoder matrix is parameterized as learnable convolutional atoms with different structural preferences, and structured reconstruction is achieved through atom responses driven by encoder-predicted coefficient maps. The WTFA module highlights the principal fault-related informative bands and their neighboring regions, thereby enhancing the specificity of feature representation. Experiments on the Case Western Reserve University dataset and the University of Ottawa variable-speed bearing dataset show that, under severe Gaussian white noise contamination (SNR = −4 dB), the proposed method achieves diagnostic accuracies of 95.94 ± 0.18% and 90.06 ± 0.25%, respectively. Under severe pink noise contamination (SNR = −4 dB), the corresponding accuracies are 92.95 ± 0.17% and 77.03 ± 0.57%, respectively. These results demonstrate that the proposed method provides stronger noise robustness than mainstream deep-learning diagnostic models.