Automation of knowledge extraction for degradation analysis

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dc.contributor.authorAddepalli, Sri
dc.contributor.authorWeyde, Tillman
dc.contributor.authorNamoano, Bernadin
dc.contributor.authorOyedeji, Oluseyi Ayodeji
dc.contributor.authorWang, Tiancheng
dc.contributor.authorErkoyuncu, John Ahmet
dc.contributor.authorRoy, Rajkumar
dc.date.accessioned2023-07-13T10:19:07Z
dc.date.available2023-07-13T10:19:07Z
dc.date.issued2023-07-13
dc.description.abstractDegradation analysis relies heavily on capturing degradation data manually and its interpretation using knowledge to deduce an assessment of the health of a component. Health monitoring requires automation of knowledge extraction to improve the analysis, quality and effectiveness over manual degradation analysis. This paper proposes a novel approach to achieve automation by combining natural language processing methods, ontology and a knowledge graph to represent the extracted degradation causality and a rule based decision-making system to enable a continuous learning process. The effectiveness of this approach is demonstrated by using an aero-engine component as a use-case.en_UK
dc.identifier.citationAddepalli S, Weyde T, Namoano B, et al., (2023) Automation of knowledge extraction for degradation analysis. CIRP Annals - Manufacturing Technology, Volume 72, Issue 1, July 2023, pp. 33-36en_UK
dc.identifier.issn0007-8506
dc.identifier.urihttps://doi.org/10.1016/j.cirp.2023.03.013
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19980
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectKnowledge managementen_UK
dc.subjectdecision makingen_UK
dc.subjectknowledge graphen_UK
dc.titleAutomation of knowledge extraction for degradation analysisen_UK
dc.typeArticleen_UK

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