DocumentCode
189462
Title
Diagnosis of PEMFC by using data-driven parity space strategy
Author
Zhongliang Li ; Outbib, Rachid ; Hissel, Daniel ; Giurgea, Stefan
Author_Institution
LSIS Lab., Univ. of Aix-Marseille, Marseille, France
fYear
2014
fDate
24-27 June 2014
Firstpage
1268
Lastpage
1273
Abstract
In this paper, a data-driven strategy is proposed for PEMFC (polymer electrolyte membrane fuel cell) diagnosis. In the strategy, parity space is directly identified from normal process data without modeling. With the identified parity space, a group residuals can be generated and evaluated to achieve fault detection. In addition, a multi-class SVM (support vector machine) is adopted to realize fault isolation. Experiments of a 40-cell stack are dedicated to highlight the approach.
Keywords
electrical engineering computing; fault diagnosis; proton exchange membrane fuel cells; support vector machines; PEMFC diagnosis; data-driven parity space strategy; fault detection; fault isolation; multiclass SVM; polymer electrolyte membrane fuel cell; support vector machine; Circuit faults; Fault detection; Fault diagnosis; Fuel cells; Hydrogen; Support vector machines; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (ECC), 2014 European
Conference_Location
Strasbourg
Print_ISBN
978-3-9524269-1-3
Type
conf
DOI
10.1109/ECC.2014.6862527
Filename
6862527
Link To Document