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J-GLOBAL ID：201702225501517208   Reference number：17A0172981

Study on the Bearing Fault Diagnosis based on Feature Selection and Probabilistic Neural Network

特性選択と確率的ニューラルネットワークに基づく軸受故障診断の研究【JST・京大機械翻訳】

Author (6)： Liu Yunzhe (Aeronautics and Astronautics Engineering Institute, Air Force Engineering University) ,  Hu Jinhai (Aeronautics and Astronautics Engineering Institute, Air Force Engineering University) ,  Ren Litong (Aeronautics and Astronautics Engineering Institute, Air Force Engineering University) ,  Yao Kaixiang (Aeronautics and Astronautics Engineering Institute, Air Force Engineering University) ,  Duan Jinfeng (Aeronautics and Astronautics Engineering Institute, Air Force Engineering University) ,  Chen Lin (Representative Office at Chengdu Aero Industry Co., Ltd. of PLA)
Material： Jixie Chuandong  (Journal of Mechanical Transmission)
Volume： 40  Issue： 10  Page： 48-53  Publication year： 2016 
JST Material Number： C2052A  ISSN： 1004-2539  CODEN： JICHEM  Document type： Article
Article type： 原著論文  Country of issue： China (CHN)  Language： CHINESE (ZH)

Abstract/Point： To improve the aero-engine fau...

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Thesaurus term： *bearing ,  feature selection ,  frequency domain ,  *fault diagnosis ,  *failure ,  support vector machine ,  evaluation criterion ,  back propagation neural network ,  time domain ,  test data ,  aero engine ,  *probabilistic neural network
Semi thesaurus term： 診断効率 ,  故障シミュレーション ,  選択法 , 【Automatic Indexing@JST】

Author keywords (5)： Aero-engine ,  Bearing ,  Fault diagnosis ,  Feature selection ,  PNN

JST classification (2)： Bearings  (QB01020W) ,  Pattern recognition  (JE07000S)

Terms in the title (4)： 確率的ニューラルネットワーク ,  軸受 ,  故障診断 ,  研究