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J-GLOBAL ID：202002250037897617   Reference number：20A0499174

A Multichannel LSTM-CNN Method for Fault Diagnosis of Chemical Process

化学プロセスの故障診断のための多チャネルLSTM-CNN法【JST・京大機械翻訳】

Author (4)： Shao Bilin ( School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China) ,  Hu Xiaoli ( School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China) ,  Bian Genqing ( School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China) ,  Zhao Yu ( School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)
Material： Mathematical Problems in Engineering (Web)
Volume： 2019  Page： Null  Publication year： 2019 
JST Material Number： U7803A  ISSN： 1024-123X  Document type： Article
Article type： 原著論文  Country of issue： United Kingdom (GBR)  Language： ENGLISH (EN)

Abstract/Point： The identification and classif...

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Thesaurus term： *fault diagnosis ,  maintenance of facility ,  convolution ,  channel ,  neural network(biomodel) ,  production process(control) ,  *failure ,  *chemical process
Semi thesaurus term： 故障分類 ,  隠れ層 ,  畳込みニューラルネットワーク , 【Automatic Indexing@JST】

JST classification (1)： Pattern recognition  (JE07000S)

Reference (32)：

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Terms in the title (5)： 化学プロセス ,  故障診断 ,  多チャネル ,  LSTM ,  CNN