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.
出处
《高压电器》
CAS
CSCD
北大核心
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)
作者简介
YANG Zhuangzhuang （1987--）,male, doctor degree candidate of electrical engineering. His current research interest is the study of the intelligent electrical apparatus and arc theory.
LIU Yang（1986--）,male, master degree candidate of electrical engineering. His current research interest is high voltage appa- ratus condition assessment.
