Rolling Bearing Fault Diagnosis based on Time-frequency Feature Parameters and Wavelet Neural Network

Abstract

In this paper, we present a novel method in fault diagnosis of rolling bearing based on time-frequency feature parameters and wavelet neural network. First, the time feature parameters are extracted from the vibration signal. Then the empirical mode decomposition (EMD) is used to decompose the signals of rolling bearings into a number of intrinsic mode functions (IMFs), and then the IMF energy-torques could be calculated through the vibration signal. Finally, those time-frequency feature parameters are taken as fault samples to train wavelet neural network (WNN)．The analysis results from the experimental show that the time- frequency feature parameters and WNN is effective in rolling bearing fault diagnosis. This paper provides the theoretical foundation for fault diagnosis in rotary machines.