标题:Gray bootstrap method for estimating frequency-varying random vibration signals with small samples
作者:Wang Yanqing; Wang Zhongyu; Sun Jianyong; Zhang Jianjun; Mourelatos, Zissimos
作者机构:[Wang Yanqing; Wang Zhongyu] Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China.; [Wang Yanqing] Shandong Univ Sci 更多
通讯作者:Wang, Yanqing
通讯作者地址:[Wang, YQ]Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China.
来源:CHINESE JOURNAL OF AERONAUTICS
出版年:2014
卷:27
期:2
页码:383-389
DOI:10.1016/j.cja.2013.07.023
关键词:Dynamic process; Estimation; Frequency-varying; Gray bootstrap method;; Random vibration signals; Small samples
摘要:During environment testing, the estimation of random vibration signals (RVS) is an important technique for the airborne platform safety and reliability. However, the available methods including extreme value envelope method (EVEM), statistical tolerances method (STM) and improved statistical tolerance method (ISTM) require large samples and typical probability distribution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method (GBM) is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated interval, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method (BM) and gray method (GM) in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level. (C) 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.
收录类别:EI;SCIE
WOS核心被引频次:10
资源类型:期刊论文
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