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兵工学报 ›› 2015, Vol. 36 ›› Issue (2): 332-336.doi: 10.3969/j.issn.1000-1093.2015.02.021

• 论文 • 上一篇    下一篇

正态应力-正态强度下可靠度精确置信下限

荣吉利1, 宋乾强1, 杨国孝2, 张涛3   

  1. (1.北京理工大学 宇航学院, 北京 100081; 2.北京理工大学 数学学院, 北京 100081; 3.北京宇航系统工程研究所北京 100076)
  • 收稿日期:2014-05-18 修回日期:2014-05-18 上线日期:2015-04-07
  • 作者简介:荣吉利(1964—),男,教授,博士生导师
  • 基金资助:
    国家部委预先研究项目(C4220062206)

The Exact Lower Confidence Limit of Reliability for Normal Stress and Normal Strength

RONG Ji-li1, SONG Qian-qiang1, YANG Guo-xiao2, ZHANG Tao3   

  1. (1.School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China;2.School of Mathematics,Beijing Institute of Technology,Beijing 100081, China;3.Beijing Institute of Space System Engineering,Beijing 100076,China)
  • Received:2014-05-18 Revised:2014-05-18 Online:2015-04-07

摘要: 针对应力和强度均服从正态分布,且二者的分布参数均未知的情况,有些学者给出了此条件下可靠度的近似置信下限,但是可靠度的近似置信下限在小样本情况下误差较大。基于应力-强度模型,结合相关统计学知识,给出可靠度的精确置信下限,同时与3种近似限方法进行了对比。结果表明:无论样本量大或者小,文中方法计算出的精度均高于3种近似限方法。当样本量较小时,推荐使用文中所给方法;当样本量较大时,可采用3种近似限方法进行近似计算。文中提出的方法可为可靠性评估提供一种新的途径。

关键词: 应用统计数学, 正态分布, 可靠性, 置信下限, 精确限

Abstract: For stress and strength obey normal distribution and their distributed parameters are unknown, some researchers have given approximate lower confidence limits of its reliability under these conditions. But the error of approximately confidence limits is large in the case of small samples. Based on stress-strength model and relevant statistical knowledge, the exact lower confidence limit of its reliability is presented. The proposed method is compared with three kinds of approximate limits methods. The result shows that no matter large or small the size of the sample is, the accuracy of the method mentioned in this paper is higher than those other three methods. When the size of the sample is small, it is recommended to use this proposed method; when the size of the sample is large, these three methods can be used for approximate calculation. This proposed method provides a new approach for the assessment of the reliability.

Key words: applied statistical mathematics, normal distribution, reliability, lower confidence limit, exact limits

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