eswa_eleni_2020.pdf (4.09 MB)
Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling
journal contribution
posted on 2020-06-02, 09:18 authored by Eleni Tsalapati, C Johnson, Tom JacksonTom Jackson, Lisa JacksonLisa Jackson, Derek Low, Ben Davies, Lei Mao, Andrew WestAndrew WestPolymer 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 ApplicationsVolume
163Publisher
ElsevierVersion
- AM (Accepted Manuscript)
Rights holder
© Elsevier LtdPublisher 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.2020.113550Acceptance date
2020-05-09Publication date
2020-07-03Copyright date
2020ISSN
0957-4174Publisher version
Language
- en
Depositor
Prof Lisa Jackson. Deposit date: 2 June 2020Article number
113550Usage metrics
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