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ID 14794
Eprint ID
14794
フルテキストURL
著者
Hanaa, E.Sayed Department of Systems Engineering, Division of Industrial Innovation Science, Okayama University
Hossam, A.Gabbar Faculty of Energy Systems and Nuclear Science, UOIT
Miyazaki, Shigeji Department of Systems Engineering, Division of Industrial Innovation Science, Okayama University
抄録
Many multivariate techniques have been applied to diagnose faults such as Principal Component Analysis (PCA), Fisher’s Discriminant Analysis (FDA), and Discriminant Partial Least Squares (DPLS). However, it has been shown that FDA and DPLS are more proficient than PCA for diagnosing faults. And recently applying kernel on FDA which is called KFDA (Kernel FDA) has showed outperformance than linear FDA based method. We propose in this research work an advanced KFDA for faults classification with Building knowledge base for faults structure using FSN. A case study is done on a chemical G-Plant process, constructed and experimental runs are done in Okayama University, Japan. The results are showing improving performance of fault detection rate for the new model over FDA.
キーワード
KFDA
Fault Diagnosis
Genetic Algorithm
Process Monitoring
発行日
2008-12
出版物タイトル
Proceedings : Fourth International Workshop on Computational Intelligence & Applications
2008巻
1号
出版者
IEEE SMC Hiroshima Chapter
開始ページ
54
終了ページ
58
資料タイプ
会議発表論文
言語
English
イベント
Fourth International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2008
イベント地
東広島市
イベント地の別言語
Higashi-Hiroshima City
論文のバージョン
publisher
査読
有り
Eprints Journal Name
IWCIA