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- W4380986096 abstract "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." @default.
- W4380986096 created "2023-06-17" @default.
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- W4380986096 date "2023-06-01" @default.
- W4380986096 modified "2023-09-25" @default.
- W4380986096 title "Automation of knowledge extraction for degradation analysis" @default.
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- W4380986096 doi "https://doi.org/10.1016/j.cirp.2023.03.013" @default.
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