NDLI: Nonlinear manifold learning based fault classification method

Content Provider	IEEE Xplore Digital Library
Author	Jiang Quansheng Lu Jiayun Jia Minping
Copyright Year	2010
Description	Author affiliation: Department of Physics and Electronics, Chaohu University, 238000, China (Jiang Quansheng; Lu Jiayun) \|\| School of Mechanical Engineering, Southeast University, Nanjing 211189, China (Jia Minping)
Abstract	In this paper, nonlinear manifold learning is introduced into equipment fault diagnosis field and solving fault pattern classification problem, a novel method of fault classification based on manifold learning is proposed. Two nonlinear manifold learning algorithms based on LLE and Laplacian Eigenmaps are utilized to learn original fault signal directly and extract intrinsic manifold feature in data set. The new approach can greatly hold the global geometry structure information embedded in the signal, and availably overcome the flaw of general feature extraction methods which only obtain data's local linear structure, obviously improve classify performance of fault classification. The experimentations on simulation and engineering instance show that, the presented method excels compared to PCA, and is a feasibly and effective technique for fault classification.
Starting Page	2972
Ending Page	2976
File Size	229286
Page Count	5
File Format	PDF
ISBN	9781424462636
e-ISBN	9787894631046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Association of Aut
Subject Keyword	Manifolds Fault diagnosis Algorithm design and analysis Manifold Learning Laplace equations LLE Fault Classification Feature extraction Classification algorithms Pattern recognition Laplacian Eigenmaps
Content Type	Text
Resource Type	Article

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