- ISSN 0258-2724
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- EI Compendex
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| Citation: | JIN Junjie, MA Zixuan, ZHANG Qi, LI Bo, YANG Wenhua, LI Ye, LAI Engang, SUN Feng. High-Fidelity Reconstruction Method for Rolling Bearing Fault Features Based on Bessel Series Empirical Wavelet Transform and Attention-Guided Denoising Autoencoder[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20250592
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To address the waveform distortion of rolling bearing fault features under strong background noise interference, a method combining Bessel series empirical wavelet transform (BSEWT) and attention-guided denoising autoencoder (AGDA) was proposed. First, the time-domain signal was mapped to a data-driven energy spectrum via Bessel basis expansion. By using a multi-scale peak detection strategy, frequency band boundaries were adaptively identified to separate modal components containing fault information. Second, an AGDA model was constructed to reconstruct the background noise distribution by using an attention weighting mechanism, reversely stripping noise components from noisy modes. Finally, the feature extraction property under five rotational speed conditions and various signal-to-noise ratio (SNR) levels was evaluated by using simulated signals and the public bearing dataset of Case Western Reserve University (CWRU). The results show that BSEWT adaptively narrows the frequency bands and finely isolates complex narrow-band interference better than the traditional empirical wavelet transform (EWT) in frequency band division. In simulation experiments with five different rotational speeds, when the input SNR exceeds 10 dB, the output SNR, root mean square error, and normalized correlation coefficient of the BSEWT-AGDA method are comprehensively superior to those of variational mode decomposition (VMD), empirical mode decomposition (EMD), and EWT. For input SNRs below −5 dB, the BSEWT method achieves the optimal output SNR index. In CWRU data validation, the signal processed by the proposed method yields a kurtosis of 8.163 5 and an impulse factor of 8.074 1, surpassing comparative algorithms like VMD and EMD. BSEWT overcomes the defect of relying on fixed frequency band division in traditional methods, achieving precise complex frequency spectrum segmentation. Combined with the AGDA model, it effectively eliminates noise interference in higher SNR ranges and achieves high-fidelity reconstruction of rolling bearing fault features.
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