Time–Frequency-Domain Fusion Cross-Attention Fault Diagnosis Method Based on Dynamic Modeling of Bearing Rotor System - Shiyu Xing; Zinan Wang; Rui Zhao; Xirui Guo; Aoxiang Liu; Wenfeng Liang (Applied Sciences)
Deep learning (DL) and machine learning (ML) have advanced rapidly. This has driven significant progress in intelligent fault diagnosis (IFD) of bearings. However, methods like self-attention have limitations. They only capture features within a single sequence. They fail to effectively extract and fuse time- and frequency-domain characteristics from raw signals. This is a critical bottleneck. To tackle this, a dual-channel cross-attention dynamic fault diagnosis network for time–frequency signals is proposed. This model’s intrinsic correlations between time-domain and frequency-domain features, which overcomes single-sequence limitations. The simulation and experimental data validate the method. It achieves over 95% diagnostic accuracy. It effectively captures complex fault patterns. This work provides a theoretical basis for better fault identification in bearing–rotor systems.