关键词: 无人机, 知识图谱, 故障诊断, 知识抽取, 双向长短期记忆网络

Abstract: In recent years, the safety and security of UAV operations have faced serious challenges, and it is crucial to ensure safe UAV operations. Based on the emerging research hotspot of knowledge mapping, this paper can make full use of UAV a priori knowledge for fault diagnosis, which can realize component association diagnosis and achieve interpretability of diagnosis results by relying on expert knowledge. At present, there are few studies on knowledge graphs for fault diagnosis, and usually“pre-training”models are used to solve the problem of insufficient data for deep learning model training. Still, the application scenarios of this method are more restricted and cannot provide valuable reference training samples for subsequent researchers. Based on the UAV fault repair manual as the primary data, a remotely supervised data annotation method based on the human school of the aircraft standard is proposed to obtain a substantial and accurately annotated UAV fault corpus, and the rule-based and BiLSTM-CRF network knowledge extraction method is combined according to the data structure characteristics, and the experiments prove that the entity extraction effect is good. Based on the UAV fault diagnosis ontology, this paper completes the construction of the UAV fault diagnosis knowledge graph, stores and visualizes it through Neo4j, and builds an intelligent question and answer system for UAS faults to provide a reasoned and accurate diagnosis for UAV faults, which proves the effectiveness of the knowledge graph in the fault diagnosis field and provides a scientific basis for the construction of the fault diagnosis system based on the knowledge graph.

Key words: unmanned air vehicle, knowledge graph, fault diagnosis, knowledge extraction, bi-directional long and short-term memory network