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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
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在python中开发和实现奇数-指数-ailamujia分布:属性和可靠性工程中的应用.

Tmader Alballa1, Qasim Ramzan2,3, Muhammad Amin4

  • 1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

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概括

一个新的奇数指数Ailamujia (OEA) 分布提供灵活的生命周期数据建模可靠性和生存分析. 它准确地适应复杂的故障模式,在现实应用中表现优于现有模型.

关键词:
艾拉穆吉亚分销公司参数估计的参数估计.Python 实现的 Python 实现可靠性分析可靠性分析统计模型 统计模型一个T-X家族的T-X家族.

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科学领域:

  • 统计 统计 统计 统计
  • 可靠性工程可靠性工程
  • 生存分析的分析.

背景情况:

  • 传统的生命周期分布往往缺乏灵活性来模拟复杂的故障模式.
  • 分布的T-X家族为创建更具适应性的统计模型提供了一个框架.

研究的目的:

  • 介绍一个新的奇数指数Ailamujia (OEA) 分布.
  • 提高复杂生命周期数据建模的灵活性.
  • 为可靠性工程和生存分析提供一个强大的工具.

主要方法:

  • 导出关键的统计属性 (时刻,MGF,特征函数等). 使用二项式和泰勒数列扩展.
  • 将难以处理的积分转换为可计算的形式,以便精确地进行分布式近似.
  • 分析了基于参数的危险率函数的行为 (增加,减少,单模式).

主要成果:

  • 与竞争型号相比,OEA分布显示出与飞机风玻璃故障数据的优越适应性.
  • 合适性测试,可靠性图表和热图证实了该模型的有效性.
  • 参数分析揭示了相关性,并控制了危险率的形状.

结论:

  • OEA分布是用于可靠性工程和生存建模的多功能和强大的工具.
  • 它有效地捕捉了现实世界的故障动态和概率预测.
  • 高效的Python实现支持可扩展的推理,用于实际应用.