Method of Mechanical Fault Intelligent Diagnosis Based on Vibration Signal of High Voltage Circuit Breaker  

作　　者：YANG Zhuangzhuang LIU Yang ZHOU Guoming LIN Xin JI Tian LI Bin 

机构地区：[1]Institute of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China [2]Liaoning ProvinceKey Laboratory of Safe Operation & Monitoring of Power Grid, Shenyang 110870, China [3]Tancheng County Power SupplyCompany of State Grid Corporation, Shandong Tancheng 276100, China

出　　处：《高压电器》2014年第4期1-6,共6页High Voltage Apparatus

基　　金：Project Supported by National Natural Science Foundation of China(51177104);Liaoning Province Natural Science Foundation of China(201102169)

摘　　要：Based on vibration signal of high voltage circuit breaker,a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine(SVM)to construct classifier for fault diagnosis is presented.The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method.The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers,and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode.Then the intelligent diagnosis network is established on the basis of the support vector machine theory.It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck,the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.Based on vibration signal of high voltage circuit breaker, a new method of intelligent fault diagnosis that wavelet packet extracts energy entropy which are used as characteristic vector of the support vector machine （SVM） to construct classifier for fault diagnosis is presented. The acceleration sensors are applied to collecting the vibration data of different states of high voltage circuit breakers based on self-made experimental platform in this method. The wavelet packet are fully applied to analyze the vibration signal and decompose vibration signal into three layers, and wavelet packet energy entropy of each frequency band are as the characteristic vector of circuit breaker failure mode. Then the intelligent diagnosis network is established on the basis of the support vector machine theory. It is verified that the method has a better capability of classification and a higher accuracy compared with the traditional neural network diagnosis method through distinguishing the three fault modes which are tripping device stuck, the vacuum arcing chamber fixed bolt looseness and too much friction force of the transmission mechanism of circuit breaker in this paper.

关 键 词：SVM energy entropy high voltage circuit breaker intelligent diagnosis 

分 类 号：TM561[电气工程—电器]