NDLI: Research on sensor fault diagnosis method based LVQ neural network and clustering analysis

Content Provider	IEEE Xplore Digital Library
Author	Tao Xu
Copyright Year	2008
Description	Author affiliation: Dept. of Autom. Control, Inst. of Aeronaut. Eng., Shenyang (Tao Xu)
Abstract	To meet the robustness of the fault diagnosis algorithm for identifying the novel fault pattern, the method, which combines the supervised classification and unsupervised classification, is proposed in this paper. As the supervised classification, Learning vector quantity neural network is employed to classify sensor mode. As the unsupervised classification, subtractive clustering is applied to identify the novel fault pattern. Finally, the applicability and effectiveness of the proposed methodology is illustrated by flow sensor data of the dynamical system. The result showed that the modal established could meet the robust requirement of fault diagnosis algorithm.
Starting Page	6017
Ending Page	6020
File Size	96634
Page Count	4
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4592854
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Artificial neural networks Classification algorithms Testing Pattern recognition Training Indexes Clustering Analysis Sensor Fault Diagnosis Neural Network
Content Type	Text
Resource Type	Article

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Research on sensor fault diagnosis method based LVQ neural network and clustering analysis