Fault diagnosis of dual-redundancy BLDC motor

In order to improve the reliability of the system, a dual-redundancy high-voltage brushless DC motor based on 270V is designed. Methods of motor fault detection and diagnosis are studied. The fault signal is analyzed by Fourier transform. For the Fourier transform, a fault detection using wavelet transform method is proposed. The current is determined to the fault detection signal based on the motor fault tree. The coif5 is selected as the wavelet basis function. Through the analysis of motor failures, the characteristics of the winding open circuit, winding short circuit, audion short circuit, audion open circuit, a phase with Hall for high and low are obtained by the coif5 wavelet function. The fault eigenvectors are obtained by the layer2 decomposition coefficients. Based on the characteristics, the wavelet neural network is selected. Multiple eigenvectors are collected by the wavelet transform. Winding short circuit and open circuit are research objects. The fault diagnosis model is established based on the BP neural network. The results showed that the two models can accurately identify the fault.