Study on Fault Signal Detection and Automatic Identification for Mine Linear Motor
Based on wavelet transform and artificial neural network, a novel method which takes advantage of both the multi-resolution decomposition of wavelet transform and the classification characteristics of artificial neural network is proposed for fault detection and identification of mine linear motor. This method consists of three stages. Firstly, according to the characteristic of unhealthy mine linear motor, the wavelet transform is carried out to decompose and reconstruct winding current signal. Then the energy of each frequency band as faulty features can be detected through spectrum analysis of wavelet coefficients about each frequency band. Secondly, with normalization method, the feature vectors are constructed from relative energy for energy of each frequency band. Finally, the feature vectors are input into neural network to identify the fault mode. The proposed method is applied to the fault diagnosis of mine linear motor, and the result of simulation proved that the wavelet neural network can effectively detect different conditions of mine linear motor and reliably identify the fault category.
Mine Linear motor Fault signal Wavelet Neural network Identification
FENG Haichao WANG Xudong XU Xiaozhuo
School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
国际会议
The 2007 International Symposium on Safety Science and Technology(2007采矿科学与安全技术国际学术会议)
河南焦作
英文
1052-1057
2007-04-17（万方平台首次上网日期，不代表论文的发表时间）