The Role of Semantic Technologies in Diagnostic and  Decision Support for Service Systems

Tsalapati, Eleni; Jackson, Thomas W.; Johnson, W.; Jackson, Lisa M.; Vasilyev, Andrey; West, Andrew A.; Mao, Lei; Davies, Benjamin

doi:10.24251/hicss.2018.196

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…This research extends the work conducted by Tsalapati et al (2018) in the following manner: 1. The PEMFC Monitoring ontology is: a) mapped to the "Sensor, Observation, Sample, and Actuator" (SOSA) ontology (Atkinson et al, 2017), which is endorsed by W3C, to establish a shareable and semantically strong ontology; b) extended to capture more details on the functionality and the specifications of the PEMFC stack and of the related sensors; c) extended to incorporate the full set of failure modes studied by Davies et al (2017), besides the water management issues; d) improved to enhance the automation of the diagnostic process; and 2.…”

Section: Introductionsupporting

confidence: 66%

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Tsalapati

Johnson

Jackson

et al. 2021

Expert Systems with Applications

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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.

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Section: Introductionsupporting

confidence: 66%

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Tsalapati

Johnson

Jackson

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…3). The web of data vision carries a wealth of practical advantages such as creating knowledge bases (Noy et al, 2019) or providing the necessary technological foundations for the development of decision support systems (Tsalapati et al, 2018;Pease et al, 2020). This has shown early dividends across various industries.…”

Section: Semantic Webmentioning

confidence: 99%

Knowledge engineering for wind energy

Marykovskiy,

Clark,

Day

et al. 2024

Wind Energ. Sci.

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Abstract. With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain and from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating them with other sources of knowledge, and making them available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning knowledge-based systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state of the art on knowledge engineering in the wind energy domain is performed with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

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“…At the same time conceptualisations and abstractions form the foundation of the Semantic web technology stack (Figure 3). The web of data vision carries a wealth of practical advantages such as creating knowledge bases (Noy et al, 2019) or providing necessary technological foundation in the development of decision support systems (Tsalapati et al, 2018;Pease et al, 2020). This has shown early dividends across various industries.…”

Section: Semantic Webmentioning

confidence: 99%

Knowledge Engineering for Wind Energy

Marykovskiy,

Clark,

Day

et al. 2024

Preprint

View full text Add to dashboard Cite

Abstract. With the rapid evolution of the wind energy sector, there is an ever-increasing need to create value from the vast amounts of data made available both from within the domain, as well as from other sectors. This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge, connecting and integrating it with other sources of knowledge, and making it available for use in next-generation artificial intelligence systems. To this end, this article highlights the role that knowledge engineering can play in the digital transformation of the wind energy sector. It presents the main concepts underpinning Knowledge-Based Systems and summarises previous work in the areas of knowledge engineering and knowledge representation in a manner that is relevant and accessible to wind energy domain experts. A systematic analysis of the current state-of-the-art on knowledge engineering in the wind energy domain is performed, with available tools put into perspective by establishing the main domain actors and their needs, as well as identifying key problematic areas. Finally, recommendations for further development and improvement are provided.

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The Role of Semantic Technologies in Diagnostic and Decision Support for Service Systems

Cited by 3 publications

References 16 publications

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Enhancing polymer electrolyte membrane fuel cell system diagnostics through semantic modelling

Knowledge engineering for wind energy

Knowledge Engineering for Wind Energy

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