Abstract
This paper proposes an image processing-based approach for the fault diagnosis of polymer electrolyte membrane (PEM) fuel cells. As more abundant information is contained in the image than that that in 1D signal, features representing PEM fuel cell faults could be better highlighted with the image. Experimental data from a PEM fuel cell system at different states, including flooding and dehydration scenarios, is used to validate the proposed method. By converting the PEM fuel cell voltage signal into a 2D grey image, several features are extracted from the image, their performance in discriminating different PEM fuel cell states is investigated, and two optimal features are determined for fault diagnosis. Moreover, the diagnostic performance of optimal features from grey image is compared with features from PEM fuel cell voltage. Results demonstrate that better diagnostic performance could be obtained with the proposed method.
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Acknowledgements
This work is supported by ‘Hundred of Talents Program of Chinese Academy of Sciences—Young Talents (2018–2050)’, and ‘Anhui Provincial Natural Science Foundation 1908085ME161’.
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Liu, Z., Pan, W., Abuker, Y.Y.A., Mao, L. (2021). A Novel Method of PEM Fuel Cell Fault Diagnosis Based on Signal-to-Image Conversion. In: Gelman, L., Martin, N., Malcolm, A.A., (Edmund) Liew, C.K. (eds) Advances in Condition Monitoring and Structural Health Monitoring. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9199-0_23
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DOI: https://doi.org/10.1007/978-981-15-9199-0_23
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