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<p>Traditional fault diagnosis methods based on supervised learning often suffer performance degradation when data distributions shift, limiting their effectiveness in real-world industrial applications. In contrast, unsupervised transfer learning can leverage knowledge from a labeled source domain to address similar tasks in an unlabeled target domain. To tackle performance drops caused by distribution shifts under varying gear loads, this paper proposes a domain-adversarial fault diagnosis method integrating multi-band filtering and multi-scale residual learning (MBF-MSRDA). Specifically, multi-band filtering is used to enhance fault features across frequency bands, while a residual network extracts multi-scale features. To reduce domain discrepancies, multi-layer maximum mean discrepancy (MMD) is applied for effective feature alignment. Experimental results validate that the proposed method achieves high fault classification accuracy under diverse load transfer conditions, demonstrating strong generalization and domain adaptability.</p>

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Domain-adversarial fault diagnosis method for rotating machinery based on multi-band filtering and multi-scale residual learning

  • 期刊论文

  • 发表日期：

    2026年5月30日

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  • Yao Tian,
  • Canfei Sun,
  • Youren Wang

摘要

Traditional fault diagnosis methods based on supervised learning often suffer performance degradation when data distributions shift, limiting their effectiveness in real-world industrial applications. In contrast, unsupervised transfer learning can leverage knowledge from a labeled source domain to address similar tasks in an unlabeled target domain. To tackle performance drops caused by distribution shifts under varying gear loads, this paper proposes a domain-adversarial fault diagnosis method integrating multi-band filtering and multi-scale residual learning (MBF-MSRDA). Specifically, multi-band filtering is used to enhance fault features across frequency bands, while a residual network extracts multi-scale features. To reduce domain discrepancies, multi-layer maximum mean discrepancy (MMD) is applied for effective feature alignment. Experimental results validate that the proposed method achieves high fault classification accuracy under diverse load transfer conditions, demonstrating strong generalization and domain adaptability.