Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling. (January 2021)

• Record Type: Journal Article
• Authors: Tsalapati, E.; Johnson, C.W.D.; Jackson, T.W.; Jackson, L.; Low, D.; Davies, B.; Mao, L.; West, A.
• Is Part Of: Expert systems with applications. Volume 163(2021)
• Publication Date: 2021-01
• Subjects: PEMFC -- Semantic technologies -- System monitoring -- Ontology Based Data Access -- Diagnostic system; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113550
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Semantic technologies are adopted for prognosis and diagnosis of PEM fuel cells. A platform is developed providing explanations for the impeding or occurring failures. The platform allows access to the end-user to retrieve implied information. The platform automatically isolates the results of unreliable sensors before the performance of any diagnostic tasks. Experimental validation of the platform against common automotive stress conditions is conducted. Abstract: 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 knowledge-based, 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.