Automation of knowledge extraction for degradation analysis

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.