Attention-Enhanced AGRU Framework for Induction Motor Incipient Fault Diagnosis in Electric Vehicles

Electric vehicles (EVs) are playing a pivotal role in transportation systems to conform to the rising exigencies for enhanced performance with safety and hindered environmental impact. Thus, to improve the efficiency and downsize the maintenance cost of EVs, an early fault diagnosis (FD) framework is crucial. Bearing and stator winding faults, which account for approximately 78% of induction machine (IM) incipient faults in EVs, remain rather elusive for conventional sensors to accurately classify them and their incipient values. To this end, this article presents a novel attention-enhanced autoencoder-gated recurrent unit (AAGRU) model that improves the accuracy and efficiency of fault analysis in IMs. Moreover, since bearing faults are usually captured through vibration sensors, which are expensive and require direct coupling with the IM, a hybrid signal reconstructor is devised based on merely stator current signals. The proposed model leverages empirical mode decomposition (EMD), fast Fourier transform (FFT), and discrete wavelet decomposition (DWT) to process the electric current data, which are then used by the AAGRU model to identify, detect, and classify the fault patterns. Experimental results demonstrate that the proposed model offers a 1%–6% improvement in fault detection and an 8%–28% improvement in interturn short-circuit (SC) fault severity classification relative to different shallow and deep-based benchmark models. The proposed model was also tested on different load conditions to ensure its applicability in practice.