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

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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]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jun90, Vol. 28 Issue 6, p1009. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Pneumatic+control+valves%22">Pneumatic control valves</searchLink><br /><searchLink fieldCode="DE" term="%22Servomechanisms%22">Servomechanisms</searchLink><br /><searchLink fieldCode="DE" term="%22Pneumatic+machinery%22">Pneumatic machinery</searchLink><br /><searchLink fieldCode="DE" term="%22System+failures%22">System failures</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Valves%22">Valves</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Boolean+algebra%22">Boolean algebra</searchLink>
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  Data: 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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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/00207549008942771
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1009
    Subjects:
      – SubjectFull: Pneumatic control valves
        Type: general
      – SubjectFull: Servomechanisms
        Type: general
      – SubjectFull: Pneumatic machinery
        Type: general
      – SubjectFull: System failures
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Valves
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Boolean algebra
        Type: general
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      – TitleFull: Failure diagnosis of a servovalve by neural networks with new learning algorithm and structure analysis.
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            NameFull: Hajime Yamashina
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            NameFull: Hiromitsu Kumamoto
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            NameFull: Susumu Okumura
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            NameFull: Takahiro Ikesaki
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          Dates:
            – D: 01
              M: 06
              Text: Jun90
              Type: published
              Y: 1990
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              Value: 28
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            – TitleFull: International Journal of Production Research
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