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使用瑞利分布和半正态分布估计应力-强度可靠性模型。

Estimate Stress-Strength Reliability Model Using Rayleigh and Half-Normal Distribution.

作者信息

Alamri Osama Abdulaziz, El-Raouf M M Abd, Ismail Eman Ahmed, Almaspoor Zahra, Alsaedi Basim S O, Khosa Saima Khan, Yusuf M

机构信息

Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia.

Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandia, Egypt.

出版信息

Comput Intell Neurosci. 2021 Jul 5;2021:7653581. doi: 10.1155/2021/7653581. eCollection 2021.

DOI:10.1155/2021/7653581
PMID:34285693
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8275412/
Abstract

In the field of life testing, it is very important to study the reliability of any component under testing. One of the most important subjects is the "stress-strength reliability" term which always refers to the quantity  ( > ) in any statistical literature. It resamples a system with random strength () that is subjected to a random strength () such that a system fails in case the stress exceeds the strength. In this study, we consider stress-strength reliability where the strength () follows Rayleigh-half-normal distribution and stress ( , , , and ) follows Rayleigh-half-normal distribution, exponential distribution, Rayleigh distribution, and half-normal distribution, respectively. This effort comprises determining the general formulations of the reliabilities of a system. Also, the maximum likelihood estimation approach and method of moment (MOM) will be utilized to estimate the parameters. Finally, reliability has been attained utilizing various values of stress and strength parameters.

摘要

在寿命测试领域，研究任何被测组件的可靠性非常重要。最重要的主题之一是“应力 - 强度可靠性”术语，在任何统计文献中它总是指数量（>）。它对具有随机强度（）的系统进行重采样，该系统受到随机应力（）的作用，使得当应力超过强度时系统失效。在本研究中，我们考虑应力 - 强度可靠性，其中强度（）服从瑞利 - 半正态分布，而应力（，，，和）分别服从瑞利 - 半正态分布、指数分布、瑞利分布和半正态分布。这项工作包括确定系统可靠性的一般公式。此外，将利用最大似然估计方法和矩量法（MOM）来估计参数。最后，利用应力和强度参数的各种值获得了可靠性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/397ec083d73e/CIN2021-7653581.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/8eef397746c7/CIN2021-7653581.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/4d8b66f71bf0/CIN2021-7653581.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/8211ef0c0bb5/CIN2021-7653581.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/e09bf6afd60d/CIN2021-7653581.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/62a6a797806f/CIN2021-7653581.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/5e40d3df15ee/CIN2021-7653581.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/c423254ab767/CIN2021-7653581.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/397ec083d73e/CIN2021-7653581.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/8eef397746c7/CIN2021-7653581.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/4d8b66f71bf0/CIN2021-7653581.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/8211ef0c0bb5/CIN2021-7653581.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/e09bf6afd60d/CIN2021-7653581.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/62a6a797806f/CIN2021-7653581.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/5e40d3df15ee/CIN2021-7653581.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/c423254ab767/CIN2021-7653581.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1935/8275412/397ec083d73e/CIN2021-7653581.008.jpg

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