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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22761

Title: Fault diagnosis of practical polymer electrolyte membrane (PEM) fuel cell system with data-driven approaches
Authors: Mao, Lei
Jackson, Lisa M.
Dunnett, Sarah J.
Keywords: Data-driven approach
Fault diagnosis
Feature selection
PEM fuel cell
Signal processing technique
Issue Date: 2016
Publisher: © The authors. Published by Wiley-VCH Verlag
Citation: MAO, L., JACKSON, L.M. and DUNNETT, S.J., 2016. Fault diagnosis of practical polymer electrolyte membrane (PEM) fuel cell system with data-driven approaches. Fuel Cells (in press)
Abstract: In this paper, data-driven approaches are applied to identify faults of a practical PEM fuel cell system. Signal processing approaches are selected and employed to multiple sensor measurements, including methodologies reducing the dimension of the original dataset, and techniques extracting features. Both supervised and unsupervised techniques are applied in this study to investigate the robustness of the diagnostic procedure. Moreover, due to the fact that a series of features can be extracted from these sensors, the singular value decomposition (SVD) technique is applied to select features providing better diagnostic performance. Results demonstrate that with features selected from SVD, fuel cell system faults can be detected more effectively, and various fuel cell faults can also be discriminated with good quality. From the findings, conclusions are made and further work suggested.
Description: This article will be open access once it is published.
Sponsor: This work is supported by grant EP/K02101X/1 from the UK Engineering and Physical Sciences Research Council (EPSRC).
Version: Accepted for publication
DOI: 10.1002/fuce.201600139
URI: https://dspace.lboro.ac.uk/2134/22761
Publisher Link: http://dx.doi.org/10.1002/fuce.201600139
ISSN: 1615-6854
Appears in Collections:Closed Access (Aeronautical and Automotive Engineering)

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