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
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;
Conference_Titel :
Control Conference (ECC), 2014 European
Conference_Location :
Strasbourg
Print_ISBN :
978-3-9524269-1-3
DOI :
10.1109/ECC.2014.6862527