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

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 gra...

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Bibliographic Details
Published in:International journal of production research 1990-06, Vol.28 (6), p.1009-1021
Main Authors: YAMASHINA, HAJIME, KUMAMOTO, HIROMITSU, OKUMURA, SUSUMU, IKESAKI, TAKAHIRO
Format: Article
Language:English
Subjects:
Applied sciences
Drives
Exact sciences and technology
Mechanical engineering. Machine design
Speed variators, torque converters. Hydraulic drives and controls, pneumatic drives and controls, fluids and components, hydraulic motors, pneumatic motors
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Summary: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.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207549008942771