Failure diagnosis of a servovalve by neural networks with new learning algorithm and structure analysis.

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Bibliographic Details
Title:	Failure diagnosis of a servovalve by neural networks with new learning algorithm and structure analysis.
Authors:	Hajime Yamashina¹, Hiromitsu Kumamoto¹, Susumu Okumura¹, Takahiro Ikesaki¹
Source:	International Journal of Production Research. Jun90, Vol. 28 Issue 6, p1009. 13p.
Subjects:	Pneumatic control valves, Servomechanisms, Pneumatic machinery, System failures, Artificial neural networks, Valves, Algorithms, Boolean algebra
Abstract:	This paper considers a failure diagnosis of a pneumatic servovalve used in automated production systems. The valve is monitored by an accelerometer. Six parameters characterizing the vibration data are extracted, and fed into neural networks to solve four types of diagnosis problems. A conjugate gradient followed by a variable metric method is demonstrated as an effective learning algorithm. Neural network structures are analysed through Boolean expressions summarizing network simulation results for given learning patterns. The neural networks are found to utilize majority voting mechanisms. Irrelevant neurons can be identified and removed without degrading the diagnosis performance. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This paper considers a failure diagnosis of a pneumatic servovalve used in automated production systems. The valve is monitored by an accelerometer. Six parameters characterizing the vibration data are extracted, and fed into neural networks to solve four types of diagnosis problems. A conjugate gradient followed by a variable metric method is demonstrated as an effective learning algorithm. Neural network structures are analysed through Boolean expressions summarizing network simulation results for given learning patterns. The neural networks are found to utilize majority voting mechanisms. Irrelevant neurons can be identified and removed without degrading the diagnosis performance. [ABSTRACT FROM AUTHOR]
ISSN:	00207543
DOI:	10.1080/00207549008942771