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

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.