SPWVD-YOLO11 Based Fault Diagnosis for Urban Rail Traction Motor Bearings Under Variable Operating Conditions

Abstract

In urban rail train traction motors, bearings serve as critical core components whose health status directly impacts traction motor operational performance and safety. Among various traction motor fault types, bearing faults have emerged as one of the most frequently occurring failure modes. However, the frequent start-stop operations and significant passenger capacity fluctuations characteristic of urban rail trains make stable operating condition data collection challenging, which has severely limited the engineering applicability of existing bearing fault diagnosis methods. This study proposes a bearing fault diagnosis method integrating SPWVD and YOLOv11: the method converts one-dimensional vibration signals into two-dimensional time–frequency maps using the SPWVD algorithm; these maps are then processed based on fault mechanisms and input into the YOLOv11 deep learning model learning and classification. Experimental results demonstrate that this method transcends the adaptability limitations of traditional time–frequency analysis under complex operating conditions and overcomes the multi-scale feature learning bottlenecks of CNN, achieving reliable bearing fault diagnosis under constant-speed conditions while maintaining over 90% accuracy in complex scenarios such as variable speed and strong noise, thereby significantly enhancing the robustness and universality of bearing fault diagnosis methods in engineering applications.