Cross-domain Rotor Fault Diagnosis Method Based on Multi-scale Attention Domain Adaptation Network

To address the issue of significant differences in data distribution between source domain and target domain, as well as the difficulty in obtaining on-site fault label samples, which lead to low diagnostic accuracy, a cross-domain rotor fault transfer diagnosis method based on multi-scale attention domain adaptation network (MADAN) was proposed. Multiscale convolutional layers were used to directly extract deep fault features of vibration signals. Spatial and channel attention were employed to capture the spatial positions and channels where important features located, and to increase the weights of process transmission, thereby further enhancing the expression ability of the features. Based on the domain adaptation method, the differences in the feature distribution extracted from the source domain and the target domain were optimized to improve the performance of transfer diagnosis. Results show that compared to other diagnostic methods, the MADAN approach achieves higher diagnostic accuracy in cross-rig rotor fault transfer tasks, providing new reference value for cross-device rotor fault transfer diagnosis.