An Integrated IEWT and CNN–Transformer Deep Architecture for Intelligent Fault Diagnosis of Bogie Axle-Box Bearings

To address the strong nonstationarity and complex multi-source interference in vibration signals of bogie axle-box bearings, a fault diagnosis method combining Improved Empirical Wavelet Transform (IEWT) and a Convolutional Neural Network (CNN)–Transformer model is proposed. First, the vibration signals are decomposed using the IEWT method, where dynamic frequency-band division adaptively determines the decomposition bands. This yields multiple intrinsic mode functions, and key modes containing fault features are selected based on information entropy. Next, the selected key modes are fused and transformed into polar coordinate projection maps, further enhancing the distinctiveness of fault data features. Finally, CNN is employed to extract local features from the vibration signals, while the Transformer captures long-range dependencies through the self-attention mechanism, significantly improving feature modeling for complex signals. To validate the fault diagnosis performance of the IEWT and CNN–Transformer model, vibration signals from bogie axle-box bearings in urban railways are analyzed. Analysis of the experimental data suggests that the adaptive decomposition of bearing signals using IEWT effectively overcomes the fixed band boundary limitations of traditional EWT, enhancing the precision of signal feature extraction. The integration of polar coordinate projection maps more accurately illustrates frequency variations and amplitude differences in the signals, fully capturing their nonstationary characteristics. Among the five fault categories of bogie axle-box bearings, the proposed method achieves an accuracy of 99.46%, a recall rate of 99.52%, and an F1-score of 0.995, significantly outperforming five classic comparison methods. This demonstrates that the combined strengths of CNN and Transformer yield higher classification accuracy and better robustness in handling complex fault patterns, effectively solving the fault diagnosis challenges for bogie axle-box bearings.