基于半监督聚类分析的无人机故障识别

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英文篇名：UAV Fault Recognition Based on Semi-supervised Clustering 作者：王楠 ; 孙善武 英文作者：WANG Nan;SUN Shan-wu;College of Management Science and Information Engineering,Jilin University of Finance and Economics;Laboratory of Logistics Industry Economy and Intelligent Logistics,Jilin University of Finance and Economics; 关键词：半监督聚类 ; 无人机 ; 模式识别 ; 故障预测 英文关键词：Semi-supervised clustering;;Unmanned aerial vehicles;;Pattern recognition;;Fault prediction 中文刊名：JSJA 英文刊名：Computer Science 机构：吉林财经大学管理科学与信息工程学院;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; 出版日期：2019-06-15 出版单位：计算机科学 年：2019 期：v.46 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 语种：中文; 页：JSJA2019S1039 页数：4 CN：S1 ISSN：50-1075/TP 分类号：202-205

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%.

引文

[1] 王海斌,葛雪雁,宋玉珍.无人机的发展趋势与技术预测[J].飞航导弹,2009(11):46-52.
    [2] 邹湘伏,何清华,贺继林.无人机发展现状及相关技术[J].飞航导弹,2006(10):9-14.
    [3] 刘重阳.国外无人机技术的发展[J].舰船电子工程,2010,30(1):19-23.
    [4] WALLACE L,LUCIEER A,WATSON C,et al.Development of a UAV-Li DAR System with Application to Forest Inventory[J].Remote Sensing,2012,4(6):1519-1543.
    [5] 何梦林.无人机的故障诊断与容错控制研究[D].贵阳:贵州大学,2015.
    [6] SHEN Y,DING S X,HAGHANI A,et al.A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process[J].Journal of Process Control,2012,22(9):1567-1581.
    [7] 郝涛,唐永哲,任玉清.BP神经网络在飞控系统传感器故障诊断中的应用[J].计算机测量与控制,2008,16(5):613-615.
    [8] 李明,徐向东.传感器故障检测的Powell神经网络方法[J].热能动力工程,2002,17(1):73-75.
    [9] 谷立臣,张优云,丘大谋.基于神经网络的传感器故障监测与诊断方法研究[J].西安交通大学学报,2002,36(9):959-962.
    [10] 王江萍.基于神经网络的信息融合故障诊断技术[J].机械科学与技术,2002,21(1):127-130.
    [11] SAMIR,ZEGHLACHE,DJAMEL,et al.Fault tolerant control based on neural network interval type-2 fuzzy sliding mode controller for octorotor UAV[J].Frontiers of Computer Science,2016,10(4):657-672.