Multisource Deep Feature Fusion of Optimized Symmetrized Dot Patterns for SRM Fault Diagnosis

This work proposes a new diagnostic method for high-resistance contact (HRC) and bearing faults in a switched reluctance motor (SRM) on the basis of multisource signal visual fusion and an intelligent classifier. First, an electric vehicle experimental platform with an SRM prototype is built, and the stator winding current and vibration signals are collected in a noninvasive manner. Second, an optimizing multisource signal symmetrized dot pattern (OMSSDP) method is proposed to characterize fault signals and realize 2-D visualization enhancement under different fault states. This method can also process multisource signal and fuse them into one image. Third, a MobileViT-ECA model is built to extract the global and local fault features of SRM. The model can effectively distinguish the type and severity degree of compound faults in SRM. Finally, comparison experiments with different time-series signal processing methods and intelligent classifiers are performed. Results prove that the proposed method can accurately identify the HRC fault phase location and bearing fault type, and its classification accuracy can reach 98.71%.