基于三参数威布尔分布的高可靠性寿命估计推论

IF 3 3区 工程技术 Q2 ENGINEERING, MECHANICAL
Xiaoyu Yang , Liyang Xie , Bowen Wang , Jianpeng Chen , Bingfeng Zhao
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引用次数: 0

摘要

吊耳是航空炸弹最关键的部件,因此对吊耳的高可靠性寿命进行估算具有重要意义。本文重点研究了吊耳疲劳数据的三参数 Weibull 分布的高可靠性寿命。本文开发了一种新方法,可根据广义似然推理生成可靠性寿命估计值,而似然推理的先验值由失效数据计算得出。根据贝叶斯理论获得后验分布,利用蒙特卡洛马尔科夫链方法计算出可靠性寿命广义信标推断的点估计和置信区间。随后,将其与非信息先验贝叶斯推断进行比较。蒙特卡罗模拟证明,所提出的方法优于非信息先验贝叶斯推断法。可靠性寿命的广义先验推断的置信度下限显示出令人满意的覆盖概率。最后,对 18 个起重吊耳进行了不同载荷下的疲劳试验。估算出了高可靠性寿命的点估计值和置信下限,从而说明了所提方法的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inference on the high-reliability lifetime estimation based on the three-parameter Weibull distribution

The high-reliability lifetime estimation of the lifting lug is of significant importance, as it is the most crucial component of the aerial bomb. This paper focuses on the high-reliability lifetime of the three-parameter Weibull distribution for lifting lug fatigue data. A novel method is developed to generate estimates of reliability lifetime according to the generalized fiducial inference, whose prior is calculated by the failure data. A posterior distribution is obtained based on Bayesian theory to compute the point estimate and the confidence interval of the generalized fiducial inference for reliability lifetime using the Monte Carlo Markov chain method. Subsequently, it is compared with the non-informative prior Bayesian inference. A Monte Carlo simulation demonstrates that the proposed method outperforms the non-informative prior Bayesian inference. The lower confidence limit of the generalized fiducial inference for the reliability lifetime exhibis satisfactory coverage probabilities. Finally, fatigue tests are performed on 18 lifting lugs under variable loads. The point estimate and the lower confidence limit of the high-reliability lifetime are estimated, which can illustrate the applicability of the proposed method.

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来源期刊
Probabilistic Engineering Mechanics
Probabilistic Engineering Mechanics 工程技术-工程:机械
CiteScore
3.80
自引率
15.40%
发文量
98
审稿时长
13.5 months
期刊介绍: This journal provides a forum for scholarly work dealing primarily with probabilistic and statistical approaches to contemporary solid/structural and fluid mechanics problems encountered in diverse technical disciplines such as aerospace, civil, marine, mechanical, and nuclear engineering. The journal aims to maintain a healthy balance between general solution techniques and problem-specific results, encouraging a fruitful exchange of ideas among disparate engineering specialities.
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