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