Addepalli, SriWeyde, TillmanNamoano, BernadinOyedeji, Oluseyi AyodejiWang, TianchengErkoyuncu, John AhmetRoy, Rajkumar2023-07-132023-07-132023-07-13Addepalli 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. 33-360007-8506https://doi.org/10.1016/j.cirp.2023.03.013https://dspace.lib.cranfield.ac.uk/handle/1826/19980Degradation 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.enAttribution 4.0 InternationalKnowledge managementdecision makingknowledge graphArticle