Multi-Source Feature Fusion Domain Adaptation Planetary Gearbox Fault Diagnosis Method

Abstract

To address the challenges of fault diagnosis in wind turbine planetary gearboxes under strong noise and limited labeled target-domain data, this paper proposes a novel intelligent diagnostic method integrating multi-source feature fusion with domain adaptation transfer learning. A Multi-source Feature Attention Fusion Convolutional Neural Network (MSFAF-CNN) is constructed, which dynamically fuses vibration signals from multiple measurement points using a channel attention mechanism to assign optimal weights to the most discriminative features. Furthermore, an improved Multi-source Local Maximum Mean Discrepancy (MS-LMMD) loss is introduced, establishing a hierarchical domain adaptation framework that enables fine-grained alignment of feature distributions between the labeled source and unlabeled target domains. Experimental results under the challenging condition of −4 dB noise demonstrate the superiority of the proposed approach: the cross-condition transfer task (A→B) achieves an accuracy of 95.32%, outperforming the conventional LMMD method by 1.05%. Finally, t-SNE-based visualization confirms that the method enhances cross-domain feature compactness, enabling direct processing of raw vibration signals without manual feature extraction. The findings indicate that the proposed approach offers a highly robust solution for fault diagnosis in drive systems under low signal-to-noise ratios and unlabeled operating conditions.