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Data-driven diagnosis of PEM fuel cell: A comparative study

Abstract : This paper is dedicated to data-driven diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC). More precisely, it deals with water related faults (flooding and membrane drying) by using pattern classification methodologies. Firstly, a method based on physical considerations is defined to label the training data. Secondly, a feature extraction procedure is carried out to pick up the significant features from vectors constructed by individual cell voltages. Finally, a classification is adopted in the feature space to realize the fault diagnosis. Various feature extraction and classification methodologies are employed on a 20-cell PEMFC stack. The performances of these methodologies are compared.
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Submitted on : Friday, February 6, 2015 - 10:11:58 AM
Last modification on : Saturday, January 15, 2022 - 3:49:21 AM
Long-term archiving on: : Thursday, May 7, 2015 - 10:06:17 AM


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  • HAL Id : hal-01113297, version 1


Zhongliang Li, Rachid Outbib, Daniel Hissel, Stefan Giurgea. Data-driven diagnosis of PEM fuel cell: A comparative study. Control Engineering Practice, Elsevier, 2014, 28, pp.1-12. ⟨hal-01113297⟩



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