改进EMD-ICA去噪在水轮机组隐蔽碰磨诊断中的应用研究

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摘要水电机组运行工况复杂，水、机、电以及噪声信号参杂耦合，早期的隐蔽的故障特征难以提取，给机组的运行造成隐患。为此，本文提出一种改进的经验模态分解（EMD）-独立分量分析（ICA）的新方法。通过经验模态分解将单通道信号分离成各个内禀模态函数（IMF），以能量波动系数为指标设定阀值，判断是否发生模态混淆。排除虚假IMF，选择与原信号相关系数较大IMF构造虚拟通道进行ICA分离，对分离信号提取其故障频率特征。以此方法指导国内某电站检修，成功排查出故障，证明此方法可以自适应、高效的去噪并提取振动故障特征，有很好的工程实用性。

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关键词 ：经验模态分解, 独立分量分析, 水电机组, 去噪, 故障诊断

Abstract：The operation condition of hydro turbines is complex,where water,mechanical,electromagnetic and noise signals are mixed and coupled,so the early concealed fault features are difficult to extract,which may lead to the risk of the unit.Accordingly, an improved EMD-ICA method was proposed.A single channel signal was separated into IMFs by EMD,and then a threshold with respect to the energy fluctuation coefficient index was set to determine the occurance of modal confusion.The false IMFs，were eliminated,a virtual channel for ICA separation was constructed by choosing those IMFs whose correlation indices with the original signal are bigger and the spectrum of the separation signal was calculated to extract the fault.By using this way,the trouble location of a domestic power plant overhaul was found successfully,which means the method has good denoising ability and can extract the characteristics of fault signals adaptively and efficiently. It can be used in practical engineering.

Key words： empirical mode decomposition(EMD) independent component analysis(ICA) hydroelectric unit denoising fault diagnosis

收稿日期: 2015-12-21 出版日期: 2017-03-15

引用本文:

郑源1,3，潘天航1，王辉斌2，葛新峰1. 改进EMD-ICA去噪在水轮机组隐蔽碰磨诊断中的应用研究[J]. 振动与冲击, 2017, 36(6): 235-240.
ZHENG Yuan1,3，PAN Tianhang1，WANG Huibin2，GE Xinfeng1，ZHANG Yizhong4. Improved EMD-ICA method used in the hidden rubbing fault diagnosis of turbine units. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(6): 235-240.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2017/V36/I6/235

[1]徐洪泉，陆力，潘罗平.“仿医疗”的水电机组故障诊断系统[J].水力发电学报，2014，33（3）：306-309.
XU Hong-quan, LU Li, PAN Luo-ping. Fault diagnosis system of hydropower unit based on imitating medical diagnosis[J].Journal of Hydroelectric Engineering, 2014,33(3):306-309.
[2]梁武科，罗兴琦，张彦宁.水力发电机组振动故障诊断系统中的信号预处理[J].水力发电学报，2003，3：114-120.
LIANG Wu-ke, LUO Xing-qi, ZHANG Yan-ning. Singnal pretreatment of vibrant fault diagnosis system of waterpower set[J].Journal of Hydroelectric Engineering, 2003,3:114-120.
[3]胥勇刚，张发启，何正嘉.独立分量分析及其在故障诊断中的应用[J].振动与冲击，2004，23（2）：104-107.
XU Yong-gang, Zhang Fa-qi, He Zheng-jia. Independent Component Analysis and Its Application to Fault Diagnosis[J].Journal of Vibration and Shock, 2004,23（2）:104-107.
[4]李宏坤，张学峰，徐福建.基于时频分析的欠定信号盲分离与微弱特征提取[J].机械工程学报，2014，50（18）：14-22.
LI Hong-kun, ZHANG Xue-feng, XU Fu-jian.Investigation on Blind Source Separation for Under-determined Mixtures Based on Time-frequency Analysis and Weak Feature Extraction[J].Journal of Mechanical Engineering, 2014,50(18):14-22.
[5]程军圣，马兴伟，杨宇.基于ICA相关系数和 VPMCD 的滚动轴承故障诊断[J].振动，测试与诊断，2015，35（4）：645-648.
CHENG Jun-sheng, MA Xing-wei, YANG Yu-ji. The Rolling Bearing Fault Diagnosis Method Based on Correlation Coefficient of Independent Component Analysis VPMCD[J].Journal of Vibration, Measurement &Diagnosis,2015,35(4),645-648.
[6]柏林，陆超，赵鑫.基于 ITD 与 ICA 的滚动轴承故障特征提取方法[J],振动与冲击，2015,34（14）：153-156.
BO Lin, LU Chao, ZHAO Xin. Fault diagnosis method for rolling bearings based on ITD and ICA[J].Journal of Vibration and Shock,2015,34(14):153-156.
[7]朱文龙，周建中，肖剑.独立分量分析–经验模态分解特征提取在水电机组振动信号中的应用[J].中国电机工程学报，2013,，33（29）：95-101.
ZHU Wen-long, ZHOU Jian-zhong, XIAO Jian. AN ICA-EMD Feature Extraction and Its application to Vibration Signals of Hydroelectric Generating Units[J].Proceedings of the CSEE,2013,33(29):95-101.
[8]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, 1998, 454:903-995．
[9]金岩.基于小波变换与独立分量分析的内燃机振声特性研究[D].杭州：浙江大学，2007.
JIN Yan. Researeh on the Characteristic of Vibration and Acoustic Signals From I.C.Engine Based on Wavelet Transform and Independent Component Analysis[D].Hangzhou:Zhejiang University,2007.
[10]焦卫东.基于独立分量分析的旋转机械故障诊断方法研究[D].杭州：浙江大学，2003.
JIAO Wei-dong. Research on Methods for Fault Diagnosis of Rotating Machines Based on Independent Component Analysis.Hangzhou:Zhejiang University,2003.
[11]J.F.Cardoso and P.Comon. Independent component analysis, a survey of some algebraic methods[J].ISCA,1996,2:93-96.
[12]杨福生，洪波.独立分量分析的原理与应用[M].北京：清华大学出版社，2006.
YANG Fusheng, HONG Bo. Principle and application of ndependent Component Analysis[M].Beijing:Tsinghua university press,2006.
[13]李辉，王瀚，白亮.改进希尔伯特–黄变换方法提取水轮机动态特征信息[J].中国电机工程学报，2011，31（2）：78-84.
LI Hui, WANG Han, BAI Liang. Dynamic Characteristic Information Extraction of Hydro turbine Based on Improved Hilbert-Huang Transform Method[J].Proceedings of the CSEE,2011,31(2):78-84.
[14]GBT6075.5-2002，在非旋转部件上测量和评价机器的机械振动第5部分：水力发电厂和泵站机组[s]．北京：中国标准出版社，2002.
GBT6075.5-2002.Mechanical Vibration-Evaluation of Machine Vibration By Measurements on Non-rotating Parts-Part 5:Machine Sets In Hydraulic Power Generating and Pumping Plants[s].Beijing:China Standards Press,2002.
[15]郑源，陈德新. 水轮机[M]. 北京：中国水利水电出版社，2005.
ZHENG Yuan, CHEN Dexin. Water Turbine[M].Beijing:China water conservancy and hydropower press,2005.