电工技术学报2017，Vol.32Issue(7)：20-30,11.
基于振动信号样本熵和相关向量机的万能式断路器分合闸故障诊断
Diagnosis on the Switching Fault of Conventional Circuit Breaker Based on Vibration Signal Sample Entropy and RVM
摘要
Abstract
In order to realize the non-invasive monitoring and diagnosis of switching fault for conventional circuit breaker,a method that combines complementary ensemble empirical mode decomposition(CEEMD)-sample entropy and relevance vector machine(RVM) is proposed for fault diagnosis by using vibration signals during the switching process.Firstly,the vibration signal is denoised by improved threshold wavelet packet denoising algorithm.Secondly,several intrinsic mode function(IMF) components which reflect main state information are extracted by CEEMD,and the top seven based on energy distribution characteristic are chosen to calculate the sample entropy as the effective feature sample.Finally,the euclidean distance of different fault samples is calculated to evaluate average sample distance between classes.The binary tree classifier based on RVM is established to diagnose the fault type.By results of contrast experiments based on designed typical switching fault models,the method realizes the conventional circuit breaker fault type identification accurately with relatively less fault data samples.And the experiments also show that it can realize the preliminary evaluation of three-phase non-synchronous fault degree based on the euclidean distance of fault samples.关键词
万能式断路器/分合闸故障诊断/振动信号/互补总体平均经验模态分解/样本熵相关向量机Key words
Conventional circuit breaker/switching fault diagnosis/vibration signal/complementary ensemble empirical mode decomposition(CEEMD)/sample entropy/relevance vector machine分类
信息技术与安全科学引用本文复制引用
孙曙光,于晗,杜太行,王景芹,赵黎媛..基于振动信号样本熵和相关向量机的万能式断路器分合闸故障诊断[J].电工技术学报,2017,32(7):20-30,11.基金项目
河北省教育厅资助科研项目(ZD2016108). （ZD2016108）
