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- W2075615314 abstract "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." @default.
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- W2075615314 date "1990-06-01" @default.
- W2075615314 modified "2023-09-22" @default.
- W2075615314 title "Failure diagnosis of a servovalve by neural networks with new learning algorithm and structure analysis" @default.
- W2075615314 cites W2012231377 @default.
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- W2075615314 doi "https://doi.org/10.1080/00207549008942771" @default.
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