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Title: Effectiveness of a novel sensor selection algorithm in PEM fuel cell on-line diagnosis
Authors: Mao, Lei
Jackson, Lisa M.
Davies, Ben
Keywords: PEM fuel cells
On-line diagnosis
Sensor selection
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Citation: MAO, L., JACKSON, L.M. and DAVIES, B., 2018. Effectiveness of a novel sensor selection algorithm in PEM fuel cell on-line diagnosis. IEEE Transactions on Industrial Electronics, doi: 10.1109/TIE.2018.2795558
Abstract: The monitoring of engineering systems is becoming more common place because of the increasing demands on reliability and safety. Being able to diagnose a fault has been facilitated by technology developments. This has resulted in the application of methods yielding an earlier detection and thus prompter mitigation of corrective measures. The level of maturity of monitoring systems varies across domain areas, with more nascent systems in newly emerging technologies, such as fuel cells. With the increasing complexity of systems comes the inclusion of more sensors, and for expedient on-line diagnosis utilizing the information from the most appropriate sensors is key to enabling excellent diagnostic resolution. In this paper, a novel sensor selection algorithm is proposed and its performance in Polymer Electrolyte Membrane (PEM) fuel cell on-line diagnosis is investigated. In the selection procedure, both sensor sensitivities to various failure modes and corresponding fuel cell degradation rates are considered. The optimal sensors determined from the proposed algorithm are compared with previous sensor selection techniques, where results show that the proposed algorithm can provide more efficient sensor selection results using less computational time, which makes this method better applied in practical PEM fuel cell systems for on-line diagnostic tasks.
Description: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Sponsor: This work was supported by the Department of Aeronautical and Automotive Engineering, Loughborough University under Grant EP/K02101X/1 from UK Engineering and Physical Sciences Research Council (EPSRC).
Version: Published
DOI: 10.1109/TIE.2018.2795558
URI: https://dspace.lboro.ac.uk/2134/28232
Publisher Link: https://doi.org/10.1109/TIE.2018.2795558
ISSN: 0278-0046
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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