DocumentCode
2420425
Title
Selection of optimal methods for intelligent process monitoring
Author
Shapovalov, Romn ; Whiteley, James R.
Author_Institution
Sch. of Chem. Eng., Oklahoma State Univ., Stillwater, OK, USA
fYear
2003
fDate
8-8 Oct. 2003
Firstpage
679
Lastpage
684
Abstract
This work proposes a statistics-based approach to the selection of the best-performing numerical methods for the detection and diagnosis of faults in the process industry. It is assumed that the performance of each method cannot be measured directly for each user-specified fault. It is shown how in those cases one can evaluate the expected performance of each method for fault detection and diagnosis by using the nonparametric statistical tests and kernel density estimation.
Keywords
chemical technology; fault diagnosis; process monitoring; statistics; fault detection; fault diagnosis; intelligent process monitoring; kernel density estimation; nonparametric statistical tests; numerical methods; process industry;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control. 2003 IEEE International Symposium on
Conference_Location
Houston, TX, USA
ISSN
2158-9860
Print_ISBN
0-7803-7891-1
Type
conf
DOI
10.1109/ISIC.2003.1254717
Filename
1254717
Link To Document