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