Research Of Motor Fault Detection System Based On Wavelet Packet Transform And Elman Neural Network

With the rapid development of modern industry, particularly after production line is widely used in industrial, the motor has become an important base of modern industrial technology. Modern motor design has not only been driven by the way how to improve their capacity, while the security, stability and reliability have also become the significant aspect cannot be ignored. Therefore, how to effectively recognize the failure occurred during the industrial production will be an important influence for the stable and orderly conduct of the process.In this work, based on the summary of traditional motor fault diagnosis methods and the analysis of acquisition and monitoring of vibration signal, we design a system based on wavelet transform and Elman neural network. And in this system, wavelet transform has been considered to be used for digital signal procession and for feature extraction, while using the pattern recognition ability of neural network to determine the status of electrical motor work.This paper analyzes the process at work in the motor common working conditions, including the shell burst, the base loose, the rotor does not work in the right place, these three common failure modes, and normal working conditions, collected by two different vibration sensor signals. A set of training the neural network is used as a sample signal; the other group used the trained neural network performance testing, as test signals. The signal eigenvectors of vibration signal for the training were extracted by wavelet packet by the, and then used for neural network training. Feature vector extraction also has been conducted to the test signal, and then passed them through the trained neural network to diagnose the situation of electrical motor work.In this paper, the design of the diagnostic system has been simulated on Matlab platform, and the system simulation is able to verify the validity and accuracy of the system. Test result is consistent with the actual test signals corresponding to different states. And from the result, we can see the diagnosis system based on wavelet transform and Elman neural network in this article is able to conduct an effective diagnosis for the working status of motor.Finally, after review the whole system design, the outlook of future work has been depicted.