Journal
FUEL CELLS
Volume 17, Issue 2, Pages 247-258Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/fuce.201600139
Keywords
Data-driven Approach; Fault Diagnosis; Feature Selection; PEM Fuel Cell; Signal Processing Technique
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Funding
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/K02101X/1]
- Engineering and Physical Sciences Research Council [EP/K02101X/1] Funding Source: researchfish
- EPSRC [EP/K02101X/1] Funding Source: UKRI
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In this paper, data-driven approaches are applied to identify faults of a practical polymer electrolyte membrane (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 sen-sors, 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 is suggested.
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