Technique of aircraft loads spectrum statistics based on kernel density estimation
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摘要: 受到实际条件限制,现阶段的飞机载荷谱实测均采取小子样实测方法,小子样实测数据是取自真实母体的一个简单样本,很难保证数据的充分性、完整性.传统的均值统计法无法对数据缺陷进行弥补.为了克服小子样数据不足问题,将核密度估计技术用于载荷谱统计,取得了良好效果.介绍了核密度估计的相关理论基础,并以某型机下沉速度谱的统计为例,详细介绍了将核密度估计方法用于载荷谱统计的数学过程.结果显示:核密度估计方法对还原载荷谱原貌、补充小子样数据的不足问题具有良好作用.Abstract: Restricted by real condition, small sample method is usually used to measure the aircraft loads spectrum. The small sample data is a simple sample from the real matrix, which can-t ensure the adequacy and the integrity of data. The traditional average statistics method which is the common method to be used to develop loads spectrum can-t compensate for the defects of data. In order to overcome the shortage of small sample data, kernel density estimation was used to static loads spectrum, and better results were gotten. The basic theory of kernel density estimation was introduced and using the statistic of an airplane-s sinking velocity spectrum as an example to describe the mathematical course of kernel density estimation method on load spectrum statistics datailedly. The results show that kernel density estimation method has a good effect for restoring the original appearance of the load spectrum and can make up the shortage of small sample data.
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Key words:
- kernel density estimation /
- loads spectrum /
- statistics /
- small sample
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[1] 陈希儒,柴根象.非参数统计教材[M].上海:华东师范大学出版社,1993 Chen Xiru,Chai Genxiang.Nonparametric estimation[M].ShangHai: East China Normal University Press, 1993 (in Chinese) [2] Epanechnikov V A. Nonparametric estimation of a multidimensional probability density[J].Theory of Probability and Application,1969,14(1):156-161 [3] Scott D W. Multivariate density estimation:theroy practice and visualization[M].New York:Wile-Blackwell,1992 [4] GJB67.6A—2008军用飞机结构强度规范:重复载荷、耐久性和损伤容限[S] GJB67.6A—2008 Military airplane structural strength specication:repeated loads,durability and damage tolerance[S](in Chinese) 期刊类型引用(8)
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