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《Control and Decision》 2009-06
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Fault diagnosis method based on empirical mode decomposition and support vector machine

SHEN Zhi-xi1,HUANG Xi-yue1,MA Xiao-xiao2(1.College of Automation,Chongqing University,Chongqing 400030,China;2.Beijing Representative Office,New Zealand Commtest Instruments Ltd,Beijing 100016,China)  
For non-stationary time series signal,a fault diagnosis method based on empirical mode decomposition(EMD)and multi-features fusion support vector machine(SVM)is proposed.Firstly,EMD is employed to decompose the original signal,the several energy-dominating intrinsic mode functions(IMFs)are chosen and the wavelet packet features of each of IMFs are extracted respectively to form several independent feature sub-spaces.Then,a series of SVM-based weak classifiers are trained in each of feature sub-spaces respectively.And a weighted fusion strategy based on the energy proportion of each of IMFs is employed to obtain stronger classifier of fault pattern.The experimental result in fault diagnosis of 6135 diesel engine shows the feasibility and effectiveness of the proposed method.
【Fund】: 国家自然科学基金项目(60443006)
【CateGory Index】: TP18
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