Multivariate Analysis for Fault Diagnosis System

Sayed Hanaa E.

Gabbar Hossam A.

Miyazaki Shigeji

Fourth International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2008

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.

KFDA

Fault Diagnosis

Genetic Algorithm

Process Monitoring

Technology. Engineering [ 500 ]

eng

IEEE SMC Hiroshima Chapter

(c) Copyright by IEEE SMC Hiroshima Chapter.

[ISSN] 1883-3977