基于DKPCA的固定翼无人机多工况在线故障诊断

TP206+.3; 固定翼无人机(UAV)执行任务过程包含多个阶段,体现出多工况特征,且UAV参数具有动态性和非线性,导致UAV在线故障诊断复杂化.本文选取UAV横向、纵向和速度控制回路的9个核心参数来表征无人机实时状态,通过动态预处理构建增广矩阵以描述UAV的动态特征;采用改进k-me-diods?算法对UAV扩维数据进行工况聚类,并采用神经网络完成在线工况匹配.针对UAV非线性特征,采用了DKPCA算法进行故障诊断,基于SPE和T2构建合成指标FAI用于故障监测,并提出了分离算法从高维数据定位故障变量;为了应对测量误差带来的故障误报,还对DKPCA合成指标FAI进行了小波去噪处理.最后,以...

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Published in西北工业大学学报 Vol. 38; no. 3; pp. 619 - 626
Main Authors 梁少军, 张世荣, 郑幸, 林冬生
Format Journal Article
LanguageChinese
Published 陆军工程大学 军械士官学校,湖北 武汉,430075%武汉大学 电气与自动化学院,湖北 武汉,430072 01.06.2020
Subjects
多工况
固定翼无人机
聚类
故障诊断
动态核主元分析
Online AccessGet full text
ISSN1000-2758
DOI10.1051/jnwpu/20203830619

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Abstract TP206+.3; 固定翼无人机(UAV)执行任务过程包含多个阶段,体现出多工况特征,且UAV参数具有动态性和非线性,导致UAV在线故障诊断复杂化.本文选取UAV横向、纵向和速度控制回路的9个核心参数来表征无人机实时状态,通过动态预处理构建增广矩阵以描述UAV的动态特征;采用改进k-me-diods?算法对UAV扩维数据进行工况聚类,并采用神经网络完成在线工况匹配.针对UAV非线性特征,采用了DKPCA算法进行故障诊断,基于SPE和T2构建合成指标FAI用于故障监测,并提出了分离算法从高维数据定位故障变量;为了应对测量误差带来的故障误报,还对DKPCA合成指标FAI进行了小波去噪处理.最后,以某UAV的实飞数据为对象,呈现以上算法的结果,验证了工况聚类、工况匹配、故障诊断及小波去噪等算法的有效性.
AbstractList TP206+.3; 固定翼无人机(UAV)执行任务过程包含多个阶段,体现出多工况特征,且UAV参数具有动态性和非线性,导致UAV在线故障诊断复杂化.本文选取UAV横向、纵向和速度控制回路的9个核心参数来表征无人机实时状态,通过动态预处理构建增广矩阵以描述UAV的动态特征;采用改进k-me-diods?算法对UAV扩维数据进行工况聚类,并采用神经网络完成在线工况匹配.针对UAV非线性特征,采用了DKPCA算法进行故障诊断,基于SPE和T2构建合成指标FAI用于故障监测,并提出了分离算法从高维数据定位故障变量;为了应对测量误差带来的故障误报,还对DKPCA合成指标FAI进行了小波去噪处理.最后,以某UAV的实飞数据为对象,呈现以上算法的结果,验证了工况聚类、工况匹配、故障诊断及小波去噪等算法的有效性.
Author 梁少军
张世荣
林冬生
郑幸
AuthorAffiliation 陆军工程大学 军械士官学校,湖北 武汉,430075%武汉大学 电气与自动化学院,湖北 武汉,430072
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Author_FL LIANG Shaojun
ZHANG Shirong
ZHENG Xing
LIN Dongsheng
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固定翼无人机
聚类
故障诊断
动态核主元分析
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Snippet TP206+.3; 固定翼无人机(UAV)执行任务过程包含多个阶段,体现出多工况特征,且UAV参数具有动态性和非线性,导致UAV在线故障诊断复杂化.本文选取UAV横向、纵向和速度控制回路...
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Title 基于DKPCA的固定翼无人机多工况在线故障诊断
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