| Citation: | WU C，ZHANG L，TANG X L，et al. Construction and application of fault knowledge graph for aero-engine lubrication system[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（4）：1336-1346 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0434
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Due to the complex structure and function of aero-engine lubrication system, the existing health management system lacks sufficient interpretability and relies heavily on expert experience for fault diagnosis A method for constructing aero-engine lubrication system fault knowledge graph was proposed in this paper. By integrating expert knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as bi-directional long short-term memory (BiLSTM) and conditional random field (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, incorporating the constructed areo-engine lubrication system fault knowledge graph, we achieved intelligent question and answer capabilities for lubrication system fault knowledge. 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 has a good application prospect in the field of intelligent fault diagnosis.
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