Noise reduction method for nonlinear signal based on maximum variance unfolding and its application to fault diagnosis

查看全文 作　　者：ZHANG Yun &LI BenWei Department of Airborne Vehicle Engineering,Naval Aeronautical Engineering Institute,Yantai 264001,China

出　　处：《Science China(Technological Sciences)》索引2010年第53卷第8期,共7页

摘　　要：A new noise reduction method for nonlinear signal based on maximum variance unfolding(MVU)is proposed.The noisy sig- nal is firstly embedded into a high-dimensional phase space based on phase space reconstruction theory,and then the manifold learning algorithm MVU is used to perform nonlinear dimensionality reduction on the data of phase space in order to separate low-dimensional manifold representing the attractor from noise subspace.Finally,the noise-reduced signal is obtained through reconstructing the low-dimensional manifold.The simulation results of Lorenz system show that the proposed MVU-based noise reduction method outperforms the KPCA-based method and has the advantages of simple parameter estimation and low parameter sensitivity.The proposed method is applied to fault detection of a vibration signal from rotor-stator of aero engine with slight rubbing fault.The denoised results show that the slight rubbing features overwhelmed by noise can be effectively extracted by the proposed noise reduction method. 关键词：nonlinear noise REDUCTION MANIFOLD learning MAXIMUM variance UNFOLDING FAULT diagnosis

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Noise reduction method for nonlinear signal based on maximum variance unfolding and its application to fault diagnosis

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