基于经验小波变换和相关向量机的断路器机械故障诊断
|
摘要
为了更准确的提取断路器故障特性,得到更可靠的故障诊断结果,在振动信号的基础上,提出了一种基于经验小波变换(Empirical Wavelet Transform,EWT)和相关向量机(Relevance Vector Machine,RVM)的断路器机械故障诊断方式。首先提取不同故障振动信号,设置阈值来初始化信号傅里叶频域分解区间,利用EWT分解得到有限带宽的多个模态。然后计算样本熵参数,计算并作为特征向量。最后,将特征向量输入相关向量机(RVM),建立不同故障的模型,对测试样本进行诊断。通过与其他方法实验对比,文中方法具有更高的故障诊断识别率,更快的识别速度。
In order to extract the circuit breaker fault characteristics more accurately and get more reliable fault diagnosis result,this paper proposed a mechanical fault diagnosis method of circuit breaker based on empirical wavelet transform (EWT) and relevance vector machine (RVW) applied to the vibration signal. Different fault vibration signals should be extracted firstly. Setting thresholds to initialize the segments of the signal in the frequency domain,and multiple modes with limited bandwidth can be obtained by EWT decomposition. Then,the sample entropies of the modes are calculated which regard as the import vector of RVM. Finally,various models of different fault is built to diagnose the test sample.Compared to other methods,this method has a higher fault diagnosis rate and faster recognition speed.
In order to extract the circuit breaker fault characteristics more accurately and get more reliable fault diagnosis result,this paper proposed a mechanical fault diagnosis method of circuit breaker based on empirical wavelet transform (EWT) and relevance vector machine (RVW) applied to the vibration signal. Different fault vibration signals should be extracted firstly. Setting thresholds to initialize the segments of the signal in the frequency domain,and multiple modes with limited bandwidth can be obtained by EWT decomposition. Then,the sample entropies of the modes are calculated which regard as the import vector of RVM. Finally,various models of different fault is built to diagnose the test sample.Compared to other methods,this method has a higher fault diagnosis rate and faster recognition speed.
引文
[1]赵书涛,张佩,申路,等.高压断路器振声联合故障诊断方法[J].电工技术学报,2014,29(7):216-219.Zhao Shutao,Zhang Pei,Shen Lu,et al.Vibration and acoustic joint mechanical fault diagnosis method of high voltage circuit breakers[J].Transactions of China Electrotechnical Society,2014,29(7):216-219.
[2]张佩,赵书涛,申路,等.基于改进EEMD的高压断路器振声联合故障诊断方法[J].电力系统保护与控制,2014,42(8):77-81.Zhang Pei,Zhao Shutao,Shen Lu,et al.Research on vibration and acoustic joint mechanical fault diagnosis method of high voltage circuit breaker based on improved EEMD[J].Power System Protection and Control,2014,42(8):77-81.
[3]孙曙光,于晗,等.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.Sun Shuguang,Yu Han,et al.Diagnosis on the Switching Fault of Conventional Circuit Breaker Based on Vibration Signal Sample Entropy and RVM[J].Transactions of China Electrotechnical Society.2017,32(7):21-30.
[4]常广,王毅,等.采用振动信号零相位滤波时频熵的高压断路器机械故障诊断[J].中国电机工程学报,2013,33(3):155-162.Chang Guang,Wang Yi,et al.Mechanical fault diagnosis of high voltage circuit breakers utilizing zero-phase filter time-frequency entropy of vibration signal[J].Proceedings of the CSEE,2013,33(3):155-162.
[5]黄建,胡晓光,巩玉楠.基于经验模态分解的高压断路器机械故障诊断方法[J].中国电机工程学报,2011,31(12):108-113.Huang Jian,Hu Xiaoguang,Gong Yunan.Machinery fault diagnosis of high voltage circuit breaker based on empirical mode decomposition[J].Proceedings of the CSEE,2011,31(12):108-113.
[6]郭谋发,徐丽兰,等.采用时频矩阵奇异值分解的配电开关振动信号特征量提取方法[J].中国电机工程学报,2014,34(28):4990-4997.Guo Moufa,Xu Lilan,et al.A vibration signal feature extraction method for distribution switches based on singular value decomposition of time-frequency matrix[J].Proceedings of the CSEE,2014,34(28):4990-4997.
[7]孙来军,胡晓光,等.改进的小波包-特征熵在高压断路器故障诊断中的应用[J].中国电机工程学报,2007,27(12):103-108.
[8]LEE D S S,LITHGOW B J,MORRISON R E.New fault diagnosis of circuit breakers[J].IEEE Transactions on Power Delivery,2003,18(2):454-459.
[9]Christopher B,Medeiros F A,Zuohua Z,et al.Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements[J].Investigative Ophthalmology&Visual Science,2005,46(4):1322-1329.
[10]GILLES J.Empirical wavelet transform[J].IEEE Trans on Signal Processing,2013,61(16):3999-4010.
[11]Liu Q,Wei Q,Fan S-Z,et al.Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery[J].Entropy,2012,14(6):978-992.
[2]张佩,赵书涛,申路,等.基于改进EEMD的高压断路器振声联合故障诊断方法[J].电力系统保护与控制,2014,42(8):77-81.Zhang Pei,Zhao Shutao,Shen Lu,et al.Research on vibration and acoustic joint mechanical fault diagnosis method of high voltage circuit breaker based on improved EEMD[J].Power System Protection and Control,2014,42(8):77-81.
[3]孙曙光,于晗,等.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.Sun Shuguang,Yu Han,et al.Diagnosis on the Switching Fault of Conventional Circuit Breaker Based on Vibration Signal Sample Entropy and RVM[J].Transactions of China Electrotechnical Society.2017,32(7):21-30.
[4]常广,王毅,等.采用振动信号零相位滤波时频熵的高压断路器机械故障诊断[J].中国电机工程学报,2013,33(3):155-162.Chang Guang,Wang Yi,et al.Mechanical fault diagnosis of high voltage circuit breakers utilizing zero-phase filter time-frequency entropy of vibration signal[J].Proceedings of the CSEE,2013,33(3):155-162.
[5]黄建,胡晓光,巩玉楠.基于经验模态分解的高压断路器机械故障诊断方法[J].中国电机工程学报,2011,31(12):108-113.Huang Jian,Hu Xiaoguang,Gong Yunan.Machinery fault diagnosis of high voltage circuit breaker based on empirical mode decomposition[J].Proceedings of the CSEE,2011,31(12):108-113.
[6]郭谋发,徐丽兰,等.采用时频矩阵奇异值分解的配电开关振动信号特征量提取方法[J].中国电机工程学报,2014,34(28):4990-4997.Guo Moufa,Xu Lilan,et al.A vibration signal feature extraction method for distribution switches based on singular value decomposition of time-frequency matrix[J].Proceedings of the CSEE,2014,34(28):4990-4997.
[7]孙来军,胡晓光,等.改进的小波包-特征熵在高压断路器故障诊断中的应用[J].中国电机工程学报,2007,27(12):103-108.
[8]LEE D S S,LITHGOW B J,MORRISON R E.New fault diagnosis of circuit breakers[J].IEEE Transactions on Power Delivery,2003,18(2):454-459.
[9]Christopher B,Medeiros F A,Zuohua Z,et al.Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements[J].Investigative Ophthalmology&Visual Science,2005,46(4):1322-1329.
[10]GILLES J.Empirical wavelet transform[J].IEEE Trans on Signal Processing,2013,61(16):3999-4010.
[11]Liu Q,Wei Q,Fan S-Z,et al.Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery[J].Entropy,2012,14(6):978-992.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |