Automation of knowledge extraction for degradation analysis
Sri Addepalli, Tillman Weyde, Bernadin Namoano, Oluseyi Ayodeji Oyedeji, Tiancheng Wang, John Ahmet Erkoyuncu, Rajkumar Roy
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.