Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (4): 94-105.doi: 10.6040/j.issn.1672-3961.0.2025.130

• Machine Learning & Data Mining • Previous Articles    

DPCA-MFF: high noise resistant bearing fault diagnosis model for complex working conditions

Wang Qimiao1, Lin Peiguang1*, Sun Mei2, Liu Lida3,4   

  1. Wang Qimiao1, Lin Peiguang1*, Sun Mei2, Liu Lida3, 4 (1. School of Computing and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250014, Shandong, China;
    2. School of Finance and Taxation, Shandong University of Finance and Economics, Jinan 250014, Shandong, China;
    3. Shandong Runyi Intelligent Technology Co., Ltd., Jinan 250022, Shandong, China;
    4. Tsinghua Shenzhen International Graduate School, Shenzhen 518055, Guangdong, China
  • Published:2026-08-12

Abstract: To address the issues of insufficient information in single-modal data, weak noise resistance, and poor cross-condition adaptability in bearing fault diagnosis under complex working conditions, an enhanced diagnostic model(DPCA-MFF)based on dynamic principal component analysis(DPCA)and multimodal feature fusion(MFF)was proposed. The DPCA was applied to adaptively reduce data dimensionality, removing noise while retaining essential features, and combined time-domain statistics with fast Fourier transform(FFT)to extract time-frequency features. Bidirectional gated recurrent unit(BiGRU)and residual network(ResNet)were employed to capture long-term temporal dependencies and spatial features from time-frequency images, respectively, with multi-head self-attention mechanisms applied to enhance feature representation. Cross-attention mechanism was introduced to dynamically fuse multimodal features. Noise interference experiments on the CWRU dataset showed that the proposed model maintained an average recognition rate above 99% under ten different types of fault signals. Under variable-speed cross-condition scenarios, the proposed model achieved an average diagnostic accuracy rate of 98.1% for fault signals of different rotational speeds and health conditions, outperforming single-modal models by about 4 to 6 percentage points and the convolutional neural network(CNN)-gated recurrent unit(GRU)fusion model by 1.7 percentage points. Even at a noise level of 1.0, the accuracy rate remained at 65.8%, which demonstrated strong performance across various noise types. The experimental results indicated that the proposed model effectively extracted comprehensive fault features through dynamic denoising, attention enhancement, and multi-modal fusion, significantly improving the noise immunity and cross-condition adaptability under complex conditions, offering a robust solution for intelligent maintenance of industrial equipment.

Key words: fault diagnosis, dynamic principal component analysis, multimodal feature fusion, cross-attention mechanism, bidirectional gate recurrent unit

CLC Number: 

  • TP277
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