+44 (0)1509 263171
Please use this identifier to cite or link to this item:
|Title: ||Effectiveness of a novel sensor selection algorithm in PEM fuel cell on-line diagnosis|
|Authors: ||Mao, Lei|
Jackson, Lisa M.
|Keywords: ||PEM fuel cells|
|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
|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).|
|Publisher Link: ||https://doi.org/10.1109/TIE.2018.2795558|
|Appears in Collections:||Published Articles (Aeronautical and Automotive Engineering)|
Files associated with this item:
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.