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排列集采样设计在电力中的效率 林德利系统可靠性估计与未经审查和正确审查的数据
Scientific reports
|July 2, 2025
概括
排序集采样 (RSS) 和极端排序集采样 (ERSS) 提供比简单随机抽样 (SRS) 更准确的系统可靠性估计. 这些方法提高了在功率林德利分布中应力强度可靠性的精度.
科学领域:
- 统计建模 统计建模
- 可靠性工程可靠性工程
- 采样理论 采样理论
背景情况:
- 准确的系统可靠性估计对于优化工业和技术系统中的资源配置至关重要.
- 在统计可靠性指标中,排序集采样 (RSS) 设计被认为是简单随机采样 (SRS) 的有效替代方案.
研究的目的:
- 调查SRS,RSS和极端排序集采样 (ERSS) 的性能,以估计应力强度可靠性.
- 为了比较这些采样设计,当应力和强度遵循独立功率林德利 (PL) 分布时.
- 在未经审查和正确审查的数据场景下评估性能.
主要方法:
- 使用最大概率 (ML) 方法对PL分布进行参数估计.
- 进行了蒙特卡洛模拟,对未经审查的数据进行了完美和不完美的排名.
- 执行模拟,对正确审查的数据进行完美的排名.
主要成果:
- 与SRS相比,RSS和ERSS给出了更精确的ML系统可靠性估计 (R).
- 在各种样本大小和参数设置中观察到RSS和ERSS的优势.
- 模拟证明了RSS和ERSS在不受审查和权利审查的条件下的有效性.
结论:
- RSS和ERSS采样设计提高了系统可靠性估计的精度.
- 这些方法有利于提高关键系统性能和成本效益的资源配置.
- 提出的方法证明了可靠性分析中适应现实数据挑战的适应性.
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