采用振动信号二维特征向量聚类的配电开关机械状态识别新方法
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摘要
配电开关动作产生的振动信号具有非线性非平稳特性,蕴含机械状态信息.提出一种基于振动信号二维特征向量和模糊K均值聚类的配电开关机械状态识别新方法.利用HHT带通滤波对配电开关振动信号进行时频分解,分别求取各子频带信号的能量值和重心频率,得到振动信号的二维特征向量作为反映配电开关的机械状态的特征量.提取配电开关在正常、底座螺丝松动、机械结构卡涩及卸掉A相触头绝缘拉杆等4种典型状态实测振动信号的二维特征向量做模糊K均值聚类,结果表明,所提取的特征向量能有效地表征配电开关的机械状态.
Vibration signals of distribution switches that contain mechanical information are characterized by nonlinearity and nonstationarity.Thus,A novel mechanical state identification method for distribution switch based on vibration signal 2-D feature vector with fuzzy K-mean clustering algorithm was proposed in this paper.Taking advantage of HHT band-pass filter,vibration signals would be decomposed in time and frequency domain in order to obtain each sub-band reconstructed signal's energy and center frequency as the 2-D feature vector,which could represent the mechanical state for distribution switch.FKM clustering was applied to these 2-D feature vectors of observed vibration signals in four typical conditions including normal states,screw loosing states,mechanical structure clamping stagnation states and relieved insulated pull rod of phase A contacts states.Results show that the feature quantity can represent the mechanical state of distribution switch accurately and effectively.
Vibration signals of distribution switches that contain mechanical information are characterized by nonlinearity and nonstationarity.Thus,A novel mechanical state identification method for distribution switch based on vibration signal 2-D feature vector with fuzzy K-mean clustering algorithm was proposed in this paper.Taking advantage of HHT band-pass filter,vibration signals would be decomposed in time and frequency domain in order to obtain each sub-band reconstructed signal's energy and center frequency as the 2-D feature vector,which could represent the mechanical state for distribution switch.FKM clustering was applied to these 2-D feature vectors of observed vibration signals in four typical conditions including normal states,screw loosing states,mechanical structure clamping stagnation states and relieved insulated pull rod of phase A contacts states.Results show that the feature quantity can represent the mechanical state of distribution switch accurately and effectively.
引文
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[16]郭谋发,刘世丹,杨耿杰.采用Hilbert谱带通滤波和暂态波形识别的谐振接地系统故障选线新方法[J].电工电能新技术,2013,32(3):67-74.
[17]李建鹏,赵书涛,夏燕青.基于双谱和希尔伯特-黄变换的断路器故障诊断方法[J].电力自动化设备,2013,33(2):115-119;125.
[18]梅飞,梅军,郑建勇,等.粒子群优化的KFCM及SVM诊断模型在断路器故障诊断中的应用[J].中国电机工程学报,2013,33(36):134-141.
[19]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3 358-3 365.
[2]杨凌霄,朱亚丽.基于概率神经网络的高压断路器故障诊断[J].电力系统保护与控制,2015,43(10):62-67.
[3]郭谋发,徐丽兰,缪希仁,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):4 990-4 997.
[4]常广,王毅,王玮.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.
[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.
[6]孙来军,胡晓光,纪延超.一种基于振动信号的高压断路器故障诊断新方法[J].中国电机工程学报,2006,26(6):157-161.
[7]彭文季,罗兴锜.基于小波包分析和支持向量机的水电机组振动故障诊断研究[J].中国电机工程学报,2006,36(24):164-168.
[8]庞尔军,王晓龙,于虹.基于虚拟仪器技术的振动测试分析系统[J].机床与液压,2014,42(7):171-173.
[9]李斌,李爽,鲁旭臣.高压断路器机械特性振动信号特征提取和故障诊断方法研究[J].高压电器,2015,51(10):138-144.
[10]周翔,王丰华,傅坚,等.基于混沌理论和K-means聚类的有载分接开关机械状态监测[J].中国电机工程学报,2015,35(6):1 541-1 548.
[11]周宏伟,周玉光,关添升,等.基于模糊k均值的变压器分接开关故障诊断[J].吉林大学学报(信息科学版),2015,33(6):719-722.
[12]HUANG N E,SHEN Z,LONG S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proceedings of the Royal Society of London,Series A:Mathematical,Physical and Engineering Sciences,1998,454(1 971):903-995.
[13]VINCENT H T,HU S J,HOU Z.Damage detection using empirical mode decomposition method and a comparison with wavelet analysis[C]//Proceedings of the Second International Workshop on Structure Health Monitoring.California:Stanford University,1999:891-900.
[14]HUANG N E,SHEN Z,LONG S R.A new view of nonlinear water waves:the Hilbert spectrum[J].Annual Review of Fluid Mechanics,1999,31(1):417-457.
[15]WU Z H,HUANG N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[16]郭谋发,刘世丹,杨耿杰.采用Hilbert谱带通滤波和暂态波形识别的谐振接地系统故障选线新方法[J].电工电能新技术,2013,32(3):67-74.
[17]李建鹏,赵书涛,夏燕青.基于双谱和希尔伯特-黄变换的断路器故障诊断方法[J].电力自动化设备,2013,33(2):115-119;125.
[18]梅飞,梅军,郑建勇,等.粒子群优化的KFCM及SVM诊断模型在断路器故障诊断中的应用[J].中国电机工程学报,2013,33(36):134-141.
[19]刘长良,武英杰,甄成刚.基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断[J].中国电机工程学报,2015,35(13):3 358-3 365.