疲劳统计学智能化的进一步研究

doi:10.7527/S1000-6893.2021.25138

固体力学与飞行器总体设计

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疲劳统计学智能化的进一步研究

徐家进¹, 高镇同²

1. 力中国际融资租赁有限公司, 广州 510620;
2. 北京航天航空大学航空科学与工程学院, 北京 100083

收稿日期:2020-12-19 修回日期:2021-02-10 出版日期:2022-08-15 发布日期:2021-02-08
通讯作者: 徐家进,E-mail:xujiajin666@163.com E-mail:xujiajin666@163.com

Further research on fatigue statistics intelligence

XU Jiajin¹, GAO Zhentong²

1. L&Z International Leasing Co. Ltd, Guangzhou 510620, China;
2. School of Aeronautical Science and Engineering, Beihang University, Beijing 100083, China

Received:2020-12-19 Revised:2021-02-10 Online:2022-08-15 Published:2021-02-08

摘要/Abstract

摘要： 《疲劳统计学智能化中的高镇同法》^[1]这篇论文仅仅是疲劳统计学的智能化研究的开始,故有必要对其做进一步的研究。其中一个重要问题是,用什么样的标准来判断样本数据是属于高斯分布还是威布尔分布。如果是属于威布尔分布,仍然可能因为算法不同而得到并不相同的3个参数,也存在哪一个威布尔分布更合适的问题。特提出以相关系数、决定系数的大小来作为一个判断标准,同时给出了相应的智能化解决方案。还研究了3参数威布尔分布的置信区间问题,对于实际应用来讲,人们最关心的是在给定的置信度及可靠度下疲劳寿命的置信区间,因此给出了在高镇同法基础上的一个智能化解决方法。

关键词: 3参数威布尔分布, 决定系数, 置信区间, 高镇同法, 疲劳统计学的智能化

Abstract: "Gao Zhentong Method in Intelligence of Statistics in Fatigue"^[1] is only the beginning of the intelligent research of fatigue statistics, so it is necessary to do further research on it. One of the important questions is, what standard is used to judge whether the sample data belongs to the Gaussian distribution or the Weibull distribution? If it belongs to the Weibull distribution, it is still possible to get three different parameters due to different algorithms. There is also the question of which Weibull distribution is more suitable. It is specially proposed to use the correlation coefficient and the size of the determination coefficient as a judgment standard, and at the same time, the corresponding intelligent solution is given. The confidence interval of the three-parameter Weibull distribution is also studied. For practical applications, people are most concerned about the confidence interval of the fatigue life under a given confidence and reliability, and an intelligent solution based on the Gao Zhentong method is given.

Key words: three-parameter Weibull distribution, determination coefficient, confidence interval, Gao Zhentong method, intelligent fatigue statistics

中图分类号:

V215.5⁺1

徐家进, 高镇同. 疲劳统计学智能化的进一步研究[J]. 航空学报, 2022, 43(8): 225138-225138.

XU Jiajin, GAO Zhentong. Further research on fatigue statistics intelligence[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 225138-225138.

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疲劳统计学智能化的进一步研究

Further research on fatigue statistics intelligence

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