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<p>Aiming at the problems of poor adaptability of heterogeneous sensing signals and feature distribution shift caused by working condition changes in cross-working-condi tion mechanical fault diagnosis, a WDH-Net model based on transfer learning is proposed for cross-working-condition heterogeneous mechanical fault diagnosis. The model integrates three core modules: time-frequency feature enhancement, dual-stream decoupling encoding and heterogeneous feature alignment. Specifically, the Wavelet-ConvNet+Cross-Attention module is used to perform adaptive wavelet packet decomposition and cross-attention feature optimization on heterogeneous time-series signals such as vibration and current to enhance the extraction of fault-sensitive features; the dual-stream decoupling encoder is adopted to separate fault features from working condition features to reduce working condition interference; a heterogeneous common feature mapping space is constructed, and the heterogeneous multi-kernel maximum mean discrepancy algorithm is combined to achieve cross-domain feature alignment and improve the generalization ability of the model. Experimental results on the public CWRU and PU datasets show that WDH-Net exhibits excellent performance in cross-working-condition fault diagnosis tasks, providing a new methodological reference for the intelligent fault diagnosis of industrial mechanical equipment.</p>

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Feature decoupling and cross domain alignment with transfer learning for cross working condition mechanical fault diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Honglian Xiao,
  • Jianhua Xiao

摘要

Aiming at the problems of poor adaptability of heterogeneous sensing signals and feature distribution shift caused by working condition changes in cross-working-condi tion mechanical fault diagnosis, a WDH-Net model based on transfer learning is proposed for cross-working-condition heterogeneous mechanical fault diagnosis. The model integrates three core modules: time-frequency feature enhancement, dual-stream decoupling encoding and heterogeneous feature alignment. Specifically, the Wavelet-ConvNet+Cross-Attention module is used to perform adaptive wavelet packet decomposition and cross-attention feature optimization on heterogeneous time-series signals such as vibration and current to enhance the extraction of fault-sensitive features; the dual-stream decoupling encoder is adopted to separate fault features from working condition features to reduce working condition interference; a heterogeneous common feature mapping space is constructed, and the heterogeneous multi-kernel maximum mean discrepancy algorithm is combined to achieve cross-domain feature alignment and improve the generalization ability of the model. Experimental results on the public CWRU and PU datasets show that WDH-Net exhibits excellent performance in cross-working-condition fault diagnosis tasks, providing a new methodological reference for the intelligent fault diagnosis of industrial mechanical equipment.