Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (9): 280-288.DOI: 10.3778/j.issn.1002-8331.2210-0415

• Engineering and Applications • Previous Articles     Next Articles

Application Method of Knowledge Graph Construction for UAV Fault Diagnosis

QIU Ling, ZHANG Ansi, ZHANG Yu, LI Shaobo, LI Chuanjiang, YANG Lei   

  1. 1.State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
    2.School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
    3.Aviation Industry Corporation of China Guizhou Aircraft Company Limited, Anshun, Guizhou 561000, China
  • Online:2023-05-01 Published:2023-05-01



  1. 1.贵州大学 计算机科学与技术学院,省部共建公共大数据国家重点实验室,贵阳 550025
    2.贵州大学 机械工程学院,贵阳 550025
    3.中航贵州飞机有限责任公司,贵州 安顺 561000

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

摘要: 近年无人机作业安全保障问题面临着严峻挑战,确保无人机安全作业至关重要。传统的无人机故障诊断方法具有容易造成知识浪费等问题,已无法满足日益复杂的工作需求,基于新兴研究热点的知识图谱,充分利用无人机先验知识进行故障诊断,可实现组件关联诊断并依靠专家知识实现诊断结果的可解释性。目前面向故障诊断知识图谱的研究较少,通常采用“预训练”模型解决深度学习模型训练的数据不足,但这种方法应用场景限制较大,且不能为后续研究者提供有价值可参考的训练样本。以无人机故障维修手册为主要数据,提出一种基于远程监督的机标人校数据标注方法,获得数量可观、标注精确的无人机故障语料库,并根据数据结构特点结合基于规则和BiLSTM-CRF网络的知识抽取方法,实验证明实体抽取效果良好。基于无人机故障诊断本体完成无人机故障诊断知识图谱的构建,通过Neo4j进行存储及可视化展示,并搭建无人机系统故障的智能问答系统,为无人机故障提供有理有据的精准诊断,证明了知识图谱在故障诊断领域的有效性,为基于知识图谱的故障诊断体系构建提供科学依据。

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