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<p>To address the challenges of low computational efficiency in existing convolutional network models and their application to rotating machinery fault detection under non-stationary operating conditions, an integrated bearing fault detection method is proposed that synthesizes time-domain, frequency-domain, and time-frequency domain features through adversarial graph convolutional learning. The core innovation lies in combining multi-domain features with adversarial GCN learning to enhance the model’s recognition and training effectiveness as well as the data’s representation capability. This method extracts time-domain and frequency-domain features of bearing signals using feature fusion technology, converts one-dimensional vibration signals into two-dimensional images via Gramian angular fields, kurtosis spectrum analysis, and chirplet transform, and integrates these images into new feature images to achieve data augmentation, thereby better participating in the model’s training tasks. Confusion matrices, T-SNE, and U-MAP are used for visual classification. Experimental results show that under three different rotational speed conditions, the average accuracy of this method in fan bearing fault diagnosis can reach 98.4 %. Compared with models such as DANN, CDAN, and swin transformer, the proposed model in this paper outperforms these models in both pruning performance and diagnostic performance. This method can provide a reference for fan bearing fault diagnosis in actual working conditions.</p>

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Research on fault diagnosis of fan bearings based on feature fusion and domain adversarial graph convolutional network

  • 期刊论文

  • 发表日期：

    2026年5月11日

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  • KunSheng Ma,
  • Weiqi Sun,
  • Lanxuan Yue,
  • Sheng Yan,
  • Ji Liu,
  • Qili Wang,
  • Jiankun Xu

摘要

To address the challenges of low computational efficiency in existing convolutional network models and their application to rotating machinery fault detection under non-stationary operating conditions, an integrated bearing fault detection method is proposed that synthesizes time-domain, frequency-domain, and time-frequency domain features through adversarial graph convolutional learning. The core innovation lies in combining multi-domain features with adversarial GCN learning to enhance the model’s recognition and training effectiveness as well as the data’s representation capability. This method extracts time-domain and frequency-domain features of bearing signals using feature fusion technology, converts one-dimensional vibration signals into two-dimensional images via Gramian angular fields, kurtosis spectrum analysis, and chirplet transform, and integrates these images into new feature images to achieve data augmentation, thereby better participating in the model’s training tasks. Confusion matrices, T-SNE, and U-MAP are used for visual classification. Experimental results show that under three different rotational speed conditions, the average accuracy of this method in fan bearing fault diagnosis can reach 98.4 %. Compared with models such as DANN, CDAN, and swin transformer, the proposed model in this paper outperforms these models in both pruning performance and diagnostic performance. This method can provide a reference for fan bearing fault diagnosis in actual working conditions.