A fault diagnosis equipment of motor bearing based on sound signal and CNN

Abstract

As an important part of mechanical equipment, the motor bearing damage rate is very high. In order to realize the fast and accurate diagnosis of motor bearing faults, this paper designs a fault diagnosis equipment based on sound signals. First, perform wavelet transform on the collected sound signal, then use the spectrogram generated by the fast Fourier transform to preliminarily determine whether the motor bearing is faulty, and finally use the convolutional neural network model that has been imported into the processor to diagnose the faulty parts of the motor bearing, The accuracy rate is above 98.41%.