Condition Monitoring of Equipment Using a Joint RSAR and Fuzzy ART Neural Network Method

Working conditions are monitoring parameters are huge and neural network learning time too long in the condition monitoring of multi word condition equipment. To improve monitoring efficiency, a joint rough set attribute reduction (RSAR) and Fuzzy ART (adaptive resonance theory) neural network metho...

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Hauptverfasser:	Jihai Gu, Xianfeng Fan, Ruoming An, Ye Tian
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Computer networks Computerized monitoring Condition monitoring Employee welfare Fuzzy logic Fuzzy neural networks Fuzzy set theory Neural networks Space technology Subspace constraints
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creator	Jihai Gu Xianfeng Fan Ruoming An Ye Tian
description	Working conditions are monitoring parameters are huge and neural network learning time too long in the condition monitoring of multi word condition equipment. To improve monitoring efficiency, a joint rough set attribute reduction (RSAR) and Fuzzy ART (adaptive resonance theory) neural network method is proposed in this study. The dimension of an input vector to Fuzzy ART neural networks can be reduced through RSAR. The updated vectors are used to train Fuzzy ART neural networks. An example is investigated to evaluate the proposed method in this study. Analysis results indicate that the proposed method can save great learning time without losing monitoring capability. Additionally, sensor abnormality and signal transmission issues may be detected as well.
doi_str_mv	10.1109/PACIIA.2008.337
format	Conference Proceeding
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To improve monitoring efficiency, a joint rough set attribute reduction (RSAR) and Fuzzy ART (adaptive resonance theory) neural network method is proposed in this study. The dimension of an input vector to Fuzzy ART neural networks can be reduced through RSAR. The updated vectors are used to train Fuzzy ART neural networks. An example is investigated to evaluate the proposed method in this study. Analysis results indicate that the proposed method can save great learning time without losing monitoring capability. Additionally, sensor abnormality and signal transmission issues may be detected as well.</abstract><pub>IEEE</pub><doi>10.1109/PACIIA.2008.337</doi><tpages>5</tpages></addata></record>
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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Computer networks Computerized monitoring Condition monitoring Employee welfare Fuzzy logic Fuzzy neural networks Fuzzy set theory Neural networks Space technology Subspace constraints
title	Condition Monitoring of Equipment Using a Joint RSAR and Fuzzy ART Neural Network Method
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