Fault Diagnosis of Rolling Bearings Based on Cross Attention Network with Multi-Scale Feature Fusion

Rolling bearings are critical in mechanical systems, and their failure can cause significant economic and safety risks. Timely fault diagnosis is essential to prevent unplanned downtime. This paper introduces a new method for rolling bearing fault diagnosis using a cross-attention network and multi-scale feature fusion. The original vibration signal is decomposed using Variational Mode Decomposition (VMD) for time-domain features, and frequency-domain features are extracted via Fast Fourier Transform (FFT). A Bidirectional Temporal Convolutional Network (BiTCN) extracts time-domain features, while a Bidirectional Gated Recurrent Unit (BiGRU) captures frequency-domain patterns. These features are integrated through a cross-attention mechanism and classified for fault identification. Evaluated on the Case Western Reserve University (CWRU) bearing dataset, the method achieves 98.67% accuracy, outperforming traditional techniques.