ID | 14794 |
Eprint ID | 14794
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FullText URL | |
Author |
Hanaa, E.Sayed
Hossam, A.Gabbar
Miyazaki, Shigeji
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Abstract | 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.
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Keywords | KFDA
Fault Diagnosis
Genetic Algorithm
Process Monitoring
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Published Date | 2008-12
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Publication Title |
Proceedings : Fourth International Workshop on Computational Intelligence & Applications
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Volume | volume2008
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Issue | issue1
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Publisher | IEEE SMC Hiroshima Chapter
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Start Page | 54
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End Page | 58
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Content Type |
Conference Paper
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language |
English
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Event Title | Fourth International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2008
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Event Location | 東広島市
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Event Location Alternative | Higashi-Hiroshima City
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File Version | publisher
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Refereed |
True
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Eprints Journal Name | IWCIA
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