机械振动下交流接触器电寿命预测失效特征量提取
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摘要
针对机械振动环境下交流接触器电寿命预测中的失效特征量进行研究,提出了一种基于优化小波去噪与核主成分分析-皮尔逊相关系数法相结合的方法以获取有效的失效特征量。首先,分别在常规与振动环境下进行交流接触器全寿命试验,并选取多个退化参数作为分析对象;其次,针对实测数据,采用多指标综合评价方法优化小波去噪,对退化参数进行去噪平滑,并结合参数的退化趋势,分析振动对接触器电寿命的影响;最后,针对退化参数及其核主成分,构建全信息特征量,计算各特征与剩余电寿命的皮尔逊相关系数。在振动环境下,结合所提方法最终提取累积燃弧能量、第一核主成分、吸合电压和接触电阻作为交流接触器电寿命预测的失效特征量。
In this paper, the failure features for electrical life prediction of AC contactor under mechanical vibration environment are studied, and a method based on optimized wavelet de-noising and kernel principal component analysis-Pearson correlation coefficient methods is proposed to obtain effective failure features. Firstly, the life cycle tests for AC contactor were carried out under normal and vibration environment respectively, and several degradation parameters were selected as the analysis objects. Secondly, aiming at the measured data, the multi-index comprehensive evaluation method was used to optimize the wavelet de-noising and then the degradation parameters were de-noised and smoothed with the optimized method. Considering the degradation trend of the parameters, the influence of vibration on the electrical life of the AC contactor was analyzed. Finally, the full information features were constructed for the degradation parameters and their kernel principal components, and Pearson correlation coefficients between the features and the residual electrical life were calculated. In the vibration environment, using the proposed method the cumulative arcing energy, first kernel principal component, closing voltage and contact resistance are finally extracted as the failure features for electrical life prediction of AC contactor.
In this paper, the failure features for electrical life prediction of AC contactor under mechanical vibration environment are studied, and a method based on optimized wavelet de-noising and kernel principal component analysis-Pearson correlation coefficient methods is proposed to obtain effective failure features. Firstly, the life cycle tests for AC contactor were carried out under normal and vibration environment respectively, and several degradation parameters were selected as the analysis objects. Secondly, aiming at the measured data, the multi-index comprehensive evaluation method was used to optimize the wavelet de-noising and then the degradation parameters were de-noised and smoothed with the optimized method. Considering the degradation trend of the parameters, the influence of vibration on the electrical life of the AC contactor was analyzed. Finally, the full information features were constructed for the degradation parameters and their kernel principal components, and Pearson correlation coefficients between the features and the residual electrical life were calculated. In the vibration environment, using the proposed method the cumulative arcing energy, first kernel principal component, closing voltage and contact resistance are finally extracted as the failure features for electrical life prediction of AC contactor.
引文
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[14] 黄勇,王克鸿,周晓晓.CO2气保焊电信号的平移不变量小波消噪[J].机械工程学报,2018,54(8):95-100.HUANG Y,WANG K H,ZHOU X X.Translation invariant wavelet de-noising of CO2 gas shielded arc welding electrical signal[J].Journal of Mechanical Engineering,2018,54(8):95-100.
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[18] 闫国强,周宁宁,张少白.基于MMTD与小波硬阈值的脑电信号去噪方法[J].系统仿真学报,2018,30(4):1490-1495.YAN G Q,ZHOU N N,ZHANG SH B.De-noising method of EEG signal based on MMTD and wavelet hard-threshold[J].Journal of System Simulation,2018,30(4):1490-1495.
[19] 李大中,赵杰,孙立江,等.果蝇优化小波阈值超声检测信号去噪[J].中国测试,2016,42(7):88-92.LI D ZH,ZHAO J,SUN L J,et al.Wavelet threshold de-noising method of ultrasonic test signal base on FOA[J].China Measurement & Test,2016,42(7):88-92.
[20] 胡嘉蕊,吕震宙.基于核主成分分析的多输出模型确认方法[J].北京航空航天大学学报,2017,43(7):1470-1480.HU J R,LV ZH ZH.Model validation method with multivariate output based on kernel principal component analysis[J].Journal of Beijing University of Aeronautics and Astronautics,2017,43(7):1470-1480.
[21] 于生宝,何建龙,王睿家,等.基于小波包分析和概率神经网络的电磁法三电平变换器故障诊断方法[J].电工技术学报,2016,31(17):102-112.YU SH B,HE J L,WANG R J,et al.Fault diagnosis of electromagnetic three-level inverter based on wavelet packet analysis and probabilistic neural networks[J].Transactions of China Electrotechnical Society,2016,31(17):102-112.
[22] 高筱婷,杨东升.基于数据驱动的智能电器运行状态监测方法[J].上海交通大学学报,2017,51(9):1104-1110.GAO X T,YANG D SH.Research on operation condition monitoring method of intellectual apparatus based on data driven[J].Journal of Shanghai Jiao Tong University,2017,51(9):1104-1110.
[23] 闫富松,李朝晖.一种基于皮尔逊相关系数识别变压器励磁涌流的方法[J].高压电器,2016,52(8):52- 56,63.YAN F S,LI CH H.Novel method to identify the inrush current based on pearson correlation coefficient[J].High Voltage Apparatus,2016,52(8):52- 56,63.
[24] 杨帆,冯翔,阮羚,等.基于皮尔逊相关系数法的水树枝与超低频介损的相关性研究[J].高压电器,2014,50(6):21- 25,31.YANG F,FENG X,RUAN L,et al.Correlation study of water tree and VLF tanδ based on Pearson correlation coefficient[J].High Voltage Apparatus,2014,50(6):21- 25,31.
[25] 王涓,吴旭鸣,王爱凤.应用皮尔逊相关系数算法查找异常电能表用户[J].电力需求侧管理,2014,16(2):52- 54.WANG J,WU X M,WANG AI F.The application of Pearson correlation coefficient algorithm in searching for the users with abnormal watt-hour meters[J].Power Demand Side Management,2014,16(2):52- 54.
[26] 中华人民共和国国家标准.GB/Z 22200- 2016,小容量交流接触器可靠性试验方法[S].北京:国家质量监督检验检疫总局,2016.Chinese National Standards.GB/Z 22200- 2016,Reliability test method for lower capacity alternating current contactors[S].Beijing:China State Bureau of Quality & Technical Supervision,2016.
[2] 李文华,马思宁,沈培根,等.振动条件下铁路继电器寿命预测研究[J].电气工程学报,2017,12(7):8-15.LI W H,MA S N,SHEN P G,et al.Study on life prediction of railway relay under vibration[J].Journal of Electrical Engineering,2017,12(7):8-15.
[3] 骆燕燕,刘旭阳,宋倩倩,等.振动应力对电连接器性能影响的预测仿真研究[J].计算机仿真,2017,34(4):396- 400,459.LUO Y Y,LIU X Y,SONG Q Q,et al.Forecast simulation study on the effect of vibration stress on the performance of electrical connectors[J].Computer Simulation,2017,34(4):396- 400,459.
[4] 骆燕燕,蔡明,于长潮,等.振动对电连接器接触性能退化的影响[J].航空学报,2017,38(8):118-129.LUO Y Y,CAI M,YU CH CH,et al.Influence of vibration on contact performance degradation of electrical connectors[J].Acta Aeronautica et Astronautica Sinica,2017,38(8):118-129.
[5] 焦玉斌,郑浩野,任万滨,等.振动环境对电磁继电器特性参数影响的试验研究[J].低压电器,2011(3):8-11,53.JIAO Y B,ZHENG H Y,REN W B,et al.Effect of vibration environment on characteristics parameters of electromagnetic relay[J].Low Voltage Apparatus,2011(3):8-11,53.
[6] 李奎,李晓倍,郑淑梅,等.基于BP神经网络的交流接触器剩余电寿命预测[J].电工技术学报,2017,32(15):120-127.LI K,LI X B,ZHENG SH M,et al.Residual electrical life prediction for AC contactor based on BP neural network[J].Transactions of China Electrotechnical Society,2017,32(15):120-127.
[7] 李奎,段宇,黄少坡,等.基于Wiener过程的交流接触器剩余电寿命预测[J].中国电机工程学报,2018,38(13):3978-3986,4039.LI K,DUAN Y,HUANG SH P,et al.Residual electrical life prediction of AC contactor based on the Wiener process [J].Proceedings of the CSEE,2018,38(13):3978-3986,4039.
[8] 李志刚,刘伯颖,李玲玲,等.基于小波包变换及RBF神经网络的继电器寿命预测[J].电工技术学报,2015,30(14):233- 240.LI ZH G,LIU B Y,LI L L,et al.Life prediction of relay based on wavelet packet transform and RBF neural network[J].Transactions of China Electrotechnical Society,2015,30(14):233- 240.
[9] 焦敬品,马婷,刘德宇,等.基于脉冲压缩技术的相邻缺陷识别方法研究[J].仪器仪表学报,2014,35(7):1614-1621.JIAO J P,MA T,LIU D Y,et al.Study on identification method of adjacent defects using pulse compression technique[J].Chinese Journal of Scientific Instrument,2014,35(7):1614-1621.
[10] 赵美蓉,于锦辉,王进,等.基于卡尔曼滤波的光纤压力信号解调方法[J].纳米技术与精密工程,2017,15(5):353-359.ZHAO M R,YU J H,WANG J,et al.A demodulation method of optical fiber pressure signal based on Kalman filtering[J].Nanotechnology and Precision Engineering,2017,15(5):353-359.
[11] 王志福,刘明春,周杨.基于模糊扩展卡尔曼滤波的轮毂电机驱动车辆纵向速度估计算法[J].西南交通大学学报,2015,50(6):1094-1099.WANG ZH F,LIU M CH,ZHOU Y.Estimation of longitudinal speed of in-wheel motor driven vehicle using fuzzy extended Kalman filter[J].Journal of Southwest Jiaotong University,2015,50(6):1094-1099.
[12] 苑玮琦,薛丹.基于机器视觉的隧道衬砌裂缝检测算法综述[J].仪器仪表学报,2017,38(12):3100-3111.YUAN W Q,XUE D.Review of tunnel lining crack detection algorithm based on machine vision[J].Chinese Journal of Scientific Instrument,2017,38(12):3100-3111.
[13] 何巍,袁亮,章翔峰.改进小波去噪-Teager算子的齿轮微弱故障提取方法[J].振动.测试与诊断,2018,38(1):155-161.HE W,YUAN L,ZHANG X F.Weak fault diagnosis method of gearbox based on improved wavelet denoising-Teager energy operator[J].Journal of Vibration,Measurement & Diagnosis,2018,38(1):155-161.
[14] 黄勇,王克鸿,周晓晓.CO2气保焊电信号的平移不变量小波消噪[J].机械工程学报,2018,54(8):95-100.HUANG Y,WANG K H,ZHOU X X.Translation invariant wavelet de-noising of CO2 gas shielded arc welding electrical signal[J].Journal of Mechanical Engineering,2018,54(8):95-100.
[15] 曹云东,高筱婷,刘树鑫,等.基于全寿命电器状态监测数据的特征信息提取[J].高电压技术,2016,42(9):2980- 2987.CAO Y D,GAO X T,LIU SH X,et al.Feature information extraction based on life-cycle condition monitoring data of switchgear[J].High Voltage Engineering,2016,42(9):2980- 2987.
[16] 李红延,周云龙,田峰,等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200- 2206.LI H Y,ZHOU Y L,TIAN F,et al.Wavelet-based vibration signal de-noising algorithm with a new adaptive threshold function[J].Chinese Journal of Scientific Instrument,2015,36(10):2200- 2206.
[17] 陈竹安,胡志峰.小波阈值改进算法的遥感图像去噪[J].测绘通报,2018(4):28-31.CHEN ZH AN,HU ZH F.Remote sensing image denoising based on improved wavelet threshold algorithm[J].Bulletin of Surveying and Mapping,2018(4):28-31.
[18] 闫国强,周宁宁,张少白.基于MMTD与小波硬阈值的脑电信号去噪方法[J].系统仿真学报,2018,30(4):1490-1495.YAN G Q,ZHOU N N,ZHANG SH B.De-noising method of EEG signal based on MMTD and wavelet hard-threshold[J].Journal of System Simulation,2018,30(4):1490-1495.
[19] 李大中,赵杰,孙立江,等.果蝇优化小波阈值超声检测信号去噪[J].中国测试,2016,42(7):88-92.LI D ZH,ZHAO J,SUN L J,et al.Wavelet threshold de-noising method of ultrasonic test signal base on FOA[J].China Measurement & Test,2016,42(7):88-92.
[20] 胡嘉蕊,吕震宙.基于核主成分分析的多输出模型确认方法[J].北京航空航天大学学报,2017,43(7):1470-1480.HU J R,LV ZH ZH.Model validation method with multivariate output based on kernel principal component analysis[J].Journal of Beijing University of Aeronautics and Astronautics,2017,43(7):1470-1480.
[21] 于生宝,何建龙,王睿家,等.基于小波包分析和概率神经网络的电磁法三电平变换器故障诊断方法[J].电工技术学报,2016,31(17):102-112.YU SH B,HE J L,WANG R J,et al.Fault diagnosis of electromagnetic three-level inverter based on wavelet packet analysis and probabilistic neural networks[J].Transactions of China Electrotechnical Society,2016,31(17):102-112.
[22] 高筱婷,杨东升.基于数据驱动的智能电器运行状态监测方法[J].上海交通大学学报,2017,51(9):1104-1110.GAO X T,YANG D SH.Research on operation condition monitoring method of intellectual apparatus based on data driven[J].Journal of Shanghai Jiao Tong University,2017,51(9):1104-1110.
[23] 闫富松,李朝晖.一种基于皮尔逊相关系数识别变压器励磁涌流的方法[J].高压电器,2016,52(8):52- 56,63.YAN F S,LI CH H.Novel method to identify the inrush current based on pearson correlation coefficient[J].High Voltage Apparatus,2016,52(8):52- 56,63.
[24] 杨帆,冯翔,阮羚,等.基于皮尔逊相关系数法的水树枝与超低频介损的相关性研究[J].高压电器,2014,50(6):21- 25,31.YANG F,FENG X,RUAN L,et al.Correlation study of water tree and VLF tanδ based on Pearson correlation coefficient[J].High Voltage Apparatus,2014,50(6):21- 25,31.
[25] 王涓,吴旭鸣,王爱凤.应用皮尔逊相关系数算法查找异常电能表用户[J].电力需求侧管理,2014,16(2):52- 54.WANG J,WU X M,WANG AI F.The application of Pearson correlation coefficient algorithm in searching for the users with abnormal watt-hour meters[J].Power Demand Side Management,2014,16(2):52- 54.
[26] 中华人民共和国国家标准.GB/Z 22200- 2016,小容量交流接触器可靠性试验方法[S].北京:国家质量监督检验检疫总局,2016.Chinese National Standards.GB/Z 22200- 2016,Reliability test method for lower capacity alternating current contactors[S].Beijing:China State Bureau of Quality & Technical Supervision,2016.