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<p>To address the limitations of single-modal approaches in bearing fault diagnosis under complex operating conditions, this study proposes SCBM-Net—a novel deep learning model based on a dual-channel multimodal fusion architecture. The model innovatively combines Continuous Wavelet Transform (CWT) and Variational Mode Decomposition (VMD) to extract complementary features from time–frequency images and temporal signals, respectively. Specifically, the first channel employs a Swin Transformer to effectively model both local and global representations of CWT-based images through a hierarchical window-based attention mechanism. The second channel adopts a CNN-BiGRU-Attention network to dynamically capture temporal dependencies from intrinsic mode functions decomposed by VMD. Features from both channels are deeply fused using a Multimodal Compact Bilinear Pooling (MCB) module, enhancing fault feature representation and overall model robustness. Experimental results on the CWRU dataset show that SCBM-Net achieves an accuracy of 99.83% under clean conditions. Even under a few-shot learning setting with only 60 training samples per class, the model still maintains a high recognition accuracy of 98.64%, demonstrating strong generalization in low-data scenarios. On an imbalanced dataset, SCBM-Net exhibits stable performance for both majority and minority classes, achieving an average accuracy of 97.33%. In a generalization test on the SEU bearing dataset, the model achieves an accuracy of 98.33%, further validating its cross-platform and cross-domain robustness and transferability. Moreover, under severe noise interference at − 10 dB, SCBM-Net retains a fault recognition accuracy of 80.67%, outperforming comparable models and demonstrating excellent noise robustness and practical applicability.</p>

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SCBM-Net: a multimodal feature fusion-based dual-channel method for bearing fault diagnosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Qiang Liu,
  • Weiyan Tong,
  • Hongwei Bai,
  • Shien Dong

摘要

To address the limitations of single-modal approaches in bearing fault diagnosis under complex operating conditions, this study proposes SCBM-Net—a novel deep learning model based on a dual-channel multimodal fusion architecture. The model innovatively combines Continuous Wavelet Transform (CWT) and Variational Mode Decomposition (VMD) to extract complementary features from time–frequency images and temporal signals, respectively. Specifically, the first channel employs a Swin Transformer to effectively model both local and global representations of CWT-based images through a hierarchical window-based attention mechanism. The second channel adopts a CNN-BiGRU-Attention network to dynamically capture temporal dependencies from intrinsic mode functions decomposed by VMD. Features from both channels are deeply fused using a Multimodal Compact Bilinear Pooling (MCB) module, enhancing fault feature representation and overall model robustness. Experimental results on the CWRU dataset show that SCBM-Net achieves an accuracy of 99.83% under clean conditions. Even under a few-shot learning setting with only 60 training samples per class, the model still maintains a high recognition accuracy of 98.64%, demonstrating strong generalization in low-data scenarios. On an imbalanced dataset, SCBM-Net exhibits stable performance for both majority and minority classes, achieving an average accuracy of 97.33%. In a generalization test on the SEU bearing dataset, the model achieves an accuracy of 98.33%, further validating its cross-platform and cross-domain robustness and transferability. Moreover, under severe noise interference at − 10 dB, SCBM-Net retains a fault recognition accuracy of 80.67%, outperforming comparable models and demonstrating excellent noise robustness and practical applicability.