基于半监督聚类分析的无人机故障识别  

UAV Fault Recognition Based on Semi-supervised Clustering

作　　者：王楠 孙善武[1,2] WANG Nan;SUN Shan-wu(College of Management Science and Information Engineering,Jilin University of Finance and Economics,Changchun 130117,China;Laboratory of Logistics Industry Economy and Intelligent Logistics,Jilin University of Finance and Economics,Changchun 130117,China)

机构地区：[1]吉林财经大学管理科学与信息工程学院,长春130117 [2]吉林财经大学物流产业经济与智能物流吉林省重点实验室,长春130117

出　　处：《计算机科学》2019年第B06期192-195,共4页Computer Science

基　　金：国家自然科学基金(61702213);吉林省教育厅“十三五”科学技术研究(JJKH20180463KJ);吉林省科技发展计划项目自然基金(20180101337JC);物流产业经济与智能物流省重点实验室开放课题基金项目(201701)资助

摘　　要：相较于有人驾驶飞行器,无人机具有诸多优势,在军事、民用及科研等领域都有着广泛应用。但是,无人机缺少飞行员的实时决策能力,因此具有较高的事故率。故障预测是无人机健康管理技术的核心,在构建故障预警模型之前,很重要的一步是对采样数据进行模式识别,进而对建模的训练数据添加精准标签,这也是完善飞行画像的一部分。文中基于沈阳某无人机生产公司大数据平台累积的无人机飞行数据,提出利用半监督聚类技术自动识别飞行过程的正常点、故障点(若故障后发生炸机,则包括炸机点)以及炸机后的点(若故障后发生炸机),在加强对飞行数据进行管理和统计的同时,进一步提高对历史飞行数据添加精准标签的效率和准确率。在真实的飞行数据或飞行测试数据上进行实验,人工验证的结果表明故障点的识别率可达到80%以上。Compared with manned vehicles,UAVs(Unmanned Aerial Vehicles)have many advantages,which make them widely used in military,civilian and scientific research fields.However,due to the lack of real-time decision-making ability,the UAV has high accident rate.Fault prediction is the core of UAV health management technology.Before building a fault prediction model,an important step is to identify the pattern of sampled data so as to add accurate labels to training data for modeling,which is also a part of improving flight portrait.Based on the UAV flight data accumulated in a big data platform of an UAV production company in Shenyang,this paper proposed a semi-supervised clustering technique to automatically identify the normal points of the flight process,the fault points(including the crashing points)and the points after crashing.At the same time,the management and statistics are strengthened,and the efficiency and accuracy of adding a precise label to the historical flights data are greatly improved.Real flight data or flight test data were used to verify the results.The results of manual verification show that the recognition rate of fault points can reach over 80%.

关 键 词：半监督聚类 无人机 模式识别 故障预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]