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利用贝叶斯推理在持续压力下的加速测试模型中,通过有序排序集采样和混合审查与实际验证验证,利用贝叶斯推理
Atef F Hashem1,2, Naif Alotaibi3, Salem A Alyami3
1Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia. affaragalla@imamu.edu.sa.
Scientific reports
|June 22, 2024
概括
本研究探讨了使用贝叶斯方法测定常压部分加速寿命测试 (CSPALTE) 中的参数估计的有序排序集采样 (ORSSA). 与混合审查下的简单随机抽样相比,ORSSA证明了可靠性分析效率的提高.
科学领域:
- 可靠性工程可靠性工程
- 统计推理 统计推理
- 加快生命测试加速生命测试
背景情况:
- 常压部分加速寿命测试 (CSPALTE) 对于产品可靠性评估至关重要.
- 传统的采样方法可能对CSPALTE中的参数估计不是最佳的.
- 贝叶斯估计为分析生命测试数据提供了一个强大的框架.
研究的目的:
- 在CSPALTE.中调查顺序排列采集样本 (ORSSA) 的应用和有效性.
- 在CSPALTE中使用贝叶斯方法估计半物流分布的参数.
- 将ORSSA的性能与混合审查下的简单随机抽样 (SRS) 进行比较.
主要方法:
- 使用贝叶斯估计与对称和不对称的损失函数.
- 员工有序排序集采样 (ORSSA) 和简单随机抽样 (SRS).
- 整合了I型混合审查和半逻辑的生活分布模型.
- 进行模拟研究,使用数值计算进行性能评估.
主要成果:
- 使用ORSSA的贝叶斯估计提供了比SRS更有效的参数估计.
- 该研究使用现实世界数据集验证了理论发现.
- 模拟结果表明ORSSA在各种场景中的优势.
结论:
- 顺序排序集采样 (ORSSA) 提高了在CSPALTE中贝叶斯参数估计的精度.
- 这些发现有助于改善压力下产品的可靠性分析方法.
- 贝叶斯式方法与ORSSA相结合,为生活测试数据分析提供了强大的工具.
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