Failure Diagnosis Using Adaptive Neural Network

Improving signal to noise ratio is a key problem to detect early faults of machinery under environment noise conditions. An effective method is presented for improving the signal to noise ratio by the adaptive neural network. This paper has made a comparison of failure detect-ability between least-m...

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Bibliographic Details
Published inTransactions of the Japan Society of Mechanical Engineers Series C Vol. 68; no. 675; pp. 3349 - 3354
Main Authors NEZU, Kikuo, SHAO, Yimin, TOKITO, Tomoya
Format Journal Article
LanguageJapanese
Published The Japan Society of Mechanical Engineers 2002
Subjects
Adaptive Filter
Machine Fault
Neural Network
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ISSN0387-5024
1884-8354
DOI10.1299/kikaic.68.3349

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Summary:Improving signal to noise ratio is a key problem to detect early faults of machinery under environment noise conditions. An effective method is presented for improving the signal to noise ratio by the adaptive neural network. This paper has made a comparison of failure detect-ability between least-mean-square (LMS) algorithm and adaptive neural network under heavy environment noise conditions. Experiment results have shown that using adaptive neural network is an effective means to extract early symptoms of machine fault under heavy environment noises and low rotating speed conditions.
ISSN:0387-5024
1884-8354
DOI:10.1299/kikaic.68.3349