sensor set paper_Mao.pdf (1.01 MB)
Effect of sensor set size on polymer electrolyte membrane fuel cell fault diagnosis
© 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
18Issue
9Citation
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
2018-08-23Notes
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
1424-8220Publisher version
Language
- en