Fault diagnosis and novel fault type detection for PEMFC system based on spherical-shaped multiple-class support vector machine

In this paper, a data-based strategy is proposed for PEMFC (polymer electrolyte membrane fuel cell) diagnosis. In the strategy, the feature extraction method Fisher Discriminant Analysis (FDA) is used firstly to extract the features from individual cell voltages. After that, the classification metho...

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Main Authors: Zhongliang Li, Giurgea, Stefan, Outbib, Rachid, Hissel, Daniel
Format: Conference Proceeding
Language:English
Subjects:
Fault diagnosis
Feature extraction
Fuel cells
Support vector machines
Training
Vectors
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Giurgea, Stefan
Outbib, Rachid
Hissel, Daniel
description In this paper, a data-based strategy is proposed for PEMFC (polymer electrolyte membrane fuel cell) diagnosis. In the strategy, the feature extraction method Fisher Discriminant Analysis (FDA) is used firstly to extract the features from individual cell voltages. After that, the classification method Spherical-Shaped Multiple-class Support Vector Machine (SSM-SVM) is used to classify the extracted features to various classes related to health states. The potential novel failure mode can be detected in the procedure. Experiments on a 40-cell stack are dedicated to verify the approach.
doi_str_mv 10.1109/AIM.2014.6878317
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subjects Fault diagnosis
Feature extraction
Fuel cells
Support vector machines
Training
Vectors
title Fault diagnosis and novel fault type detection for PEMFC system based on spherical-shaped multiple-class support vector machine
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