NDLI: New method of fault feature extraction based on supervised LLE

Content Provider	IEEE Xplore Digital Library
Author	Quansheng Jiang Jiayun Lu Minping Jia
Copyright Year	2010
Description	Author affiliation: School of Mechanical Engineering, Southeast University, Nanjing 211189, China (Minping Jia) \|\| Department of Physics, Chaohu University, 238000, China (Quansheng Jiang; Jiayun Lu)
Abstract	The Locally Linear Embedding (LLE) is one of the efficient nonlinear dimensionality reduction techniques, which can be used to fault feature extraction. But it is not taking the class information of the data into account. In this paper, we propose a novel approach of feature extraction based on supervised LLE algorithm. Via utilizing class information to guide the procedure of nonlinear mapping, the Supervised LLE enhances local within-class relations and help to classification. The approach uses the Supervised LLE to extract feature for class labels data, and utilizes RBF network to map the unlabeled data to the feature space, which easily implement fault pattern classification. The experiments on benchmark dataset and engineering instance demonstrate that, the proposed approach excels compared to PCA and LLE, and it is an accurate technique for classification.
Starting Page	1727
Ending Page	1731
File Size	155992
Page Count	5
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498459
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Chaos Laplace equations Pattern classification Supervised LLE Feature extraction Linear discriminant analysis Artificial intelligence Machinery Machine intelligence Principal component analysis Nonlinear dimensionality reduction
Content Type	Text
Resource Type	Article

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