Asynchronous Motor Fault Diagnosis Based on Wavelet Neural Network

According to the mapping relationship between the common symptoms of fault in the asynchronous motor and fault mode, this paper established asynchronous motor fault diagnosis model by using the wavelet neural network (WNN). The model adopts the conjugate gradient descent algorithm, which is optimized by the momentum and adaptive learning rate. The initialization of parameters of the WNN is also analyzed in this paper. The final simulation results verified that, compared with conventional wavelet neural network and BP network, this model significantly reduces the training time and is valid for motor fault diagnosis. Keywordswavelet neural network; fault diagnosis; conjugate gradient descent algorithm; parameters initialization