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Automation of knowledge extraction for degradation analysis

Addepalli, S., Weyde, T. ORCID: 0000-0001-8028-9905, Namoano, B. , Oyedeji, O. A., Wang, T., Erkoyuncu, J. A. & Roy, R. ORCID: 0000-0001-5491-7437 (2023). Automation of knowledge extraction for degradation analysis. CIRP Annals, 72(1), pp. 33-36. doi: 10.1016/j.cirp.2023.03.013


Degradation 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.

Publication Type: Article
Additional Information: This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: Knowledge management, Decision making, Knowledge graph
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology > Computer Science
SWORD Depositor:
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