Study of the Fault Diagnosis Based on Wavelet and Neural Network for the Motor

Abstract

In the motor fault diagnosis technology, vibration signals can fully reflect the motor operation conditions. In this paper, a linear motor fault diagnosis method based on wavelet packet and neural network was presented. The improved neural network system was designed with variable hidden layer neurons. The network chosen different numerical values depending on different situations to reach the requirements that improving diagnostic accuracy and shortening the diagnosis time. The linear motor’s normal and two common faults vibration signals were analyzed and the vibration signals energy characteristics were extracted through wavelet packet, then identified fault through neural network. The experimental results show that this method can effectively improve the motor fault diagnosis accuracy.