ニューラルネットワークフィルタを用いた空気圧電磁弁の音響故障診断 Fault Diagnosis System of Electromagnetic Valve Using Neural Network Filter

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  This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection.<br>

収録刊行物

  • 日本原子力学会和文論文誌 = Transactions of the Atomic Energy Society of Japan  

    日本原子力学会和文論文誌 = Transactions of the Atomic Energy Society of Japan 7(3), 186-193, 2008-09-01 

    Atomic Energy Society of Japan

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各種コード

  • NII論文ID(NAID)
    10021307502
  • NII書誌ID(NCID)
    AA11643165
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    13472879
  • NDL 記事登録ID
    9629307
  • NDL 雑誌分類
    ZN36(科学技術--原子力工学・工業)
  • NDL 請求記号
    Z74-C788
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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