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|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|
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, 17 (2), pp. 247–258.|
|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: ||Closed access until 24 November 2017|
|Sponsor: ||This work is supported by grant EP/K02101X/1 from the UK Engineering and Physical Sciences Research Council (EPSRC).|
|Version: ||Accepted for publication|
|Publisher Link: ||http://dx.doi.org/10.1002/fuce.201600139|
|Related Resource: ||https://dx.doi.org/10.17028/rd.lboro.4009959|
|Appears in Collections:||Closed Access (Aeronautical and Automotive Engineering)|
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