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
発行日
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