Fault diagnosis plays a very important role in ensuring the stable operation of motor.Therefore,fault diagnosis is a hot topic in current research.In this study,the short-time Fourier transform is used to transform the one-dimensional vibration sig-nal into a two-dimensional time-frequency diagram,so as to solve the nonlinear and instability problems of the vibration signal of the motor bearing.As the input of the convolutional neural network,the sample data set is formed through the direct extraction of the fault feature signal.The fault diagnosis model is established by convolution neural network and softmax multi-classifier,and the ac-curacy of the algorithm optimization is verified in Python,which proves that the algorithm can improve the accuracy of motor fault di-agnosis.
convolutional neural networksoftmax multi-classifierfault diagnosisshort time Fourier transform
国家科技期刊平台
NSTL
万方数据
