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Online Diagnosis of PEMFC by Combining Support Vector Machine and Fluidic Model
Author(s) -
Li Z.,
Giurgea S.,
Outbib R.,
Hissel D.
Publication year - 2014
Publication title -
fuel cells
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.485
H-Index - 69
eISSN - 1615-6854
pISSN - 1615-6846
DOI - 10.1002/fuce.201300197
Subject(s) - support vector machine , stack (abstract data type) , computer science , proton exchange membrane fuel cell , linear discriminant analysis , artificial intelligence , fluidics , classifier (uml) , machine learning , pattern recognition (psychology) , data mining , engineering , fuel cells , chemical engineering , programming language , aerospace engineering
This paper deals with the online fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) stack. In the proposed approach, support vector machine (SVM) and a fluidic model are correlated to realize the diagnosis of the faults on water management. With the help of the fluidic model, health states of the stack can be identified. This procedure is then dedicated to labeling the training data, which is used to train the dimension reduction model, named Fisher discriminant analysis (FDA), and the SVM classifier. The online diagnosis can then be realized by using the trained FDA and SVM models. The proposed approach is illustrated by using the experimental data of a 20‐cell PEMFC stack. The feasibility of the approach for online implementation is also affirmed.