Fault diagnosis of induction motor based on current signal and EMD-MSDP-CNN

The induction motor serves as the core power component in new energy vehicle drive systems. Any malfunction of the drive motor will directly undermine the operational reliability of the vehicle, potentially leading to drive system failures or, in severe cases, significant safety hazards endangering the lives of drivers and passengers. To address the issue of faint fault signatures within motor current signals, an image-based fault characterization method synergized empirical mode decomposition (EMD) with modified symmetrized dot pattern (MSDP) was proposed. Subsequently, a dedicated convolutional neural network (CNN) architecture with automated image feature extraction capabilities was engineered to classify motor health conditions, including healthy operation, bearing fault, and rotor broken bar. Finally, the efficiency and precision of the EMD-MSDP-CNN model for induction motor fault diagnosis were validated through experimental data, demonstrating a robust average diagnostic accuracy of 93.21% across diverse fault scenarios under multiple operating conditions.