NDLI: An intelligent pattern recognition method for machine fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Linfeng Deng Rongzhen Zhao
Copyright Year	2013
Description	Author affiliation: Sch. of Mech. & Electron. Eng., Lanzhou Univ. of Technol., Lanzhou, China (Linfeng Deng; Rongzhen Zhao)
Abstract	This paper proposes a novel pattern recognition method for rotating machine fault diagnosis. In this work, the proposed method firstly employs the local mean decomposition (LMD) algorithm to decompose the raw vibration signals into a small number of product functions (PFs), and then, the energy of each useful PF is computed and normalized to form an original feature vector, so an original data table about machine faults can be constructed via these feature vectors; subsequently, the table are processed using kernel principal component analysis (KPCA) to extract the principal feature and compute the corresponding feature values; lastly, the low-dimensional features and their values are input into least squares support vector machine (LS-SVM) for fault identification. The experimental results show that the proposed method can effectively extract the fault features and can accurately identify the different machine faults.
Starting Page	187
Ending Page	192
File Size	586598
Page Count	6
File Format	PDF
e-ISBN	9781479911974
DOI	10.1109/URAI.2013.6677339
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-30
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Support vector machines Vibrations Local mean decomposition Intelligent pattern recognition Machine fault diagnosis Kernel principal component analysis Feature extraction Vectors Least squares support machine Kernel Principal component analysis
Content Type	Text
Resource Type	Article

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