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Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

journal contribution
posted on 02.06.2020 by Eleni Tsalapati, C Johnson, Tom Jackson, Lisa Jackson, Derek Low, Ben Davies, Lei Mao, Andrew West
Polymer electrolyte membrane fuel cells (PEMFC) are a promising technology for economic and environmentally friendly energy production. However, they haven’t reached their full potential in the market yet as only few reliable PEMFC systems have successfully passed the prototyping face. A drawback of the current diagnostic tools is that only a select few are of high genericity, reliability and can perform efficiently on-line at the same time. Furthermore, there is only limited research identifying both PEMFC stack faults and ancillary system faults simultaneously. While none of the existing tools can be interrogated by the end-user. In this research, we develop novel artificial intelligence-based technologies to overcome these existing barriers, i.e., i) a semantically enriched integrating schema (ontology) of the overall operation and structure of the PEMFC that allows automatic inference engines to automatically deduce fault detection; ii) a knowledgebased, light-weight, on-line fuel cell system diagnosis (FuCSyDi) platform. FuCSyDi detects and provides the location of failures by considering only the data from the reliable sensors. Additionally, it provides the reasons underpinning any forthcoming failures and enables the end-user to interrogate the platform for further information regarding its operation and structure. Our platform is validated by performing tests against common automotive stress conditions. This innovative approach enhances the reliability of the fuel cell system diagnosis and, hence, its lifetime performance

Funding

Adaptive Informatics for Intelligent Manufacturing (AI2M) : EP/K014137/1

Robust Lifestyle Design and Health Monitoring for Fuel-Cell Extended Performance (RESILIENCE) : EP/K02101X/1

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering
  • Business and Economics
  • Mechanical, Electrical and Manufacturing Engineering

Department

  • Aeronautical and Automotive Engineering
  • Business

Published in

Expert Systems with Applications

Volume

163

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier Ltd

Publisher statement

This paper was accepted for publication in the journal Expert Systems with Applications and the definitive published version is available at https://doi.org/10.1016/j.eswa.2020.113550

Acceptance date

09/05/2020

Publication date

2020-07-03

Copyright date

2020

ISSN

0957-4174

Language

en

Depositor

Prof Lisa Jackson. Deposit date: 2 June 2020

Article number

113550

Exports