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Effect of sensor set size on polymer electrolyte membrane fuel cell fault diagnosis

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posted on 21.03.2019 by Lei Mao, Lisa Jackson
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents a comparative study on the performance of different sizes of sensor sets on polymer electrolyte membrane (PEM) fuel cell fault diagnosis. The effectiveness of three sizes of sensor sets, including fuel cell voltage only, all the available sensors, and selected optimal sensors in detecting and isolating fuel cell faults (e.g., cell flooding and membrane dehydration) are investigated using the test data from a PEM fuel cell system. Wavelet packet transform and kernel principal component analysis are employed to reduce the dimensions of the dataset and extract features for state classification. Results demonstrate that the selected optimal sensors can provide the best diagnostic performance, where different fuel cell faults can be detected and isolated with good quality.

Funding

This research was funded by UK Engineering and Physical Sciences Research Council (EPSRC) with number of EP/K02101X/1, and Hundreds of Talents Program of Chinese Academy of Sciences—Young Talents

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Sensors (Switzerland)

Volume

18

Issue

9

Citation

MAO, L. and JACKSON, L.M., 2018. Effect of sensor set size on polymer electrolyte membrane fuel cell fault diagnosis. Sensors, 18: 18, 2777.

Publisher

© The Authors. Published by MDPI.

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Publication date

2018-08-23

Notes

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

ISSN

1424-8220

Language

en

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