Synergistic TransGCN for Aeroengine Bearing Skidding Diagnosis Under Time-Varying Conditions

The demand for bearing skidding diagnosis is widely present in aeroengines operating at high-speed and light-load conditions. However, the weak and time-varying characteristics of skidding signal raise challenges for accurate diagnosis. To address these issues, we propose a synergistic TransGCN strategy to extract rich feature information from time-varying weak bearing skidding signals. Unlike existing methods, the prior knowledge obtained from bearing skidding analysis and the alternate integration and synergistic optimization of various advantages are used to enhance algorithm performance. First, an adaptive chirplet transform is designed to measure the time-varying cage slip rate. Second, the skidding sensitive characteristics are determined, and the variation ranges of slip rate sensitivity are employed as prior knowledge to calculate the fusion weights of multisource information. Then, an unsupervised deep feature representation network is constructed to analyze the complex correlation of bearing skidding signals. Finally, a synergistic TransGCN is developed by alternately integrating and synergistic optimizing Bayesformer and graph convolutional network. The superiority of the proposed strategy has been verified.