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Joint Feature and Model Selection for SVM Fault Diagnosis in Solid Oxide Fuel Cell Systems.
- Source :
-
Mathematical Problems in Engineering . 5/19/2015, Vol. 2015, p1-12. 12p. - Publication Year :
- 2015
-
Abstract
- This paper describes an original technique for the joint feature and model selection in the context of support vector machine (SVM) classification applied as a diagnosis strategy in model-based fault detection and isolation (FDI). We demonstrate that the proposed technique contributes to the solution of an open research problem: to design a robust FDI procedure, correctly functioning with different operating conditions and fault sizes, specifically settled for an electric generation system based on solid oxide fuel cells (SOFCs). By using a quantitative model of the generation system coupled to an optimized SVM classifier, a satisfactory FDI procedure is achieved, which is robust against modeling and measurement errors and is compliant with practical deployment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1024123X
- Volume :
- 2015
- Database :
- Academic Search Index
- Journal :
- Mathematical Problems in Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 109250430
- Full Text :
- https://doi.org/10.1155/2015/282547