VWM-DCRNN: A Method of Combining Signal Processing With Deep Learning for Fault Diagnosis in Single-Phase PWM Rectifier

Based on variational mode decomposition and dual model of convolutional recurrent neural network (VMD-DCRNN), a novel diagnosis method is proposed to discover the faults of insulated gate bipolar transistors (IGBTs), diodes, and series resonant circuit in a single-phase pulsewidth modulation ac–dc rectifier, an important part of traction power supply system of high-speed railway. By virtue of the combination of signal processing and deep learning schemes, the multiscale feature information for fault diagnosis is extracted by regarding the input current of the rectifier and the voltage of dc side as the original signal. More clearly, VMD is adopted to decompose the original signal to a series of intrinsic mode function components (IMFs). Then, the submodel of current is designed to detect the fault types of IGBTs and diodes. Meanwhile, the submodel of voltage is established to identify the fault types of series resonant circuit. These IMFs are fed into DCRNN consisting of the two submodels for training and testing. Finally, the features extracted from the two submodels are integrated and converted to label space to complete fault diagnosis. Experimental results show that the proposed VMD-DCRNN algorithm is superior to the single-channel models, reaching 96.27%.