Abstract
Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
