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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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
Online AccessGet full text
ISSN0387-5024
1884-8354
DOI10.1299/kikaic.68.3349

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Abstract 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.
AbstractList 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.
Author SHAO, Yimin
TOKITO, Tomoya
NEZU, Kikuo
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References (2) Shao, Y. and Nezu, K., "An On-Line Monitoring and Diagnostic Method of Rolling Element Bearing with AI", Trans. Soc. Instrument Control Eng., 32-8 (1996), 1287-1293.
(6) 西川緯一•北村新三,ニューラルネットと計測制御,(1995),1-220,朝倉書店.
(1) Shao, Y. and Nezu, K., "Self-aligning Roller Bearing Fault•Detection Using Asynchronous Adaptive Noise Cancelling", JSME Int. J., SEIES C, 42-1 (1999), 33-43.
(4) 辻井重男•久保田一•古川利博,適応信号処理,(1995),4-27,昭晃堂.
(7) Stronge, W. J., Impact Mechanics, (2000), 86-115, Cambridge University Press.
(5) 中島智,適応フィルタを用いたころがり軸受の音響診断,(1999),41-46,設備管理学会.
(3) 前川健二•中島智•豊田利夫,衝撃振動を利用した機械部品の劣化度評価法,日本設備管理学会誌,9-3(1997),3-8.
References_xml – reference: (1) Shao, Y. and Nezu, K., "Self-aligning Roller Bearing Fault•Detection Using Asynchronous Adaptive Noise Cancelling", JSME Int. J., SEIES C, 42-1 (1999), 33-43.
– reference: (5) 中島智,適応フィルタを用いたころがり軸受の音響診断,(1999),41-46,設備管理学会.
– reference: (3) 前川健二•中島智•豊田利夫,衝撃振動を利用した機械部品の劣化度評価法,日本設備管理学会誌,9-3(1997),3-8.
– reference: (4) 辻井重男•久保田一•古川利博,適応信号処理,(1995),4-27,昭晃堂.
– reference: (2) Shao, Y. and Nezu, K., "An On-Line Monitoring and Diagnostic Method of Rolling Element Bearing with AI", Trans. Soc. Instrument Control Eng., 32-8 (1996), 1287-1293.
– reference: (7) Stronge, W. J., Impact Mechanics, (2000), 86-115, Cambridge University Press.
– reference: (6) 西川緯一•北村新三,ニューラルネットと計測制御,(1995),1-220,朝倉書店.
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Snippet 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...
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StartPage 3349
SubjectTerms Adaptive Filter
Machine Fault
Neural Network
Title Failure Diagnosis Using Adaptive Neural Network
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