基于维纳过程和韦布尔分布的竞争性故障模型的统计推断
1School of Mathematics-physics and Finance, Anhui Polytechnic University, Wuhu 241000, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
本研究引入了一种分析竞争性故障模型的新方法,该模型包括渐进性退化和突然故障. 一般化的枢纽数量方法为关键参数和可靠性指标提供可靠的置信区间.
科学领域:
- 可靠性工程可靠性工程
- 统计建模 统计建模
- 风险分析 风险分析
背景情况:
- 在实践中,竞争性故障模型越来越重要,包括退化现象和突然故障.
- 准确的统计建模对于理解复杂故障模式下的系统可靠性至关重要.
研究的目的:
- 提出和研究用于建模涉及退化和突然故障的竞争性故障的通用枢纽数量方法.
- 导出模型参数和可靠性函数的点和间隔估计.
主要方法:
- 使用维纳过程建模降解故障和使用韦布尔分布突然故障.
- 使用维纳过程参数的最大概率估计 ($ \ mu $, $ \ sigma^2 $).
- 为韦布尔参数 ($ \eta $, $ \beta $) 导出反向估计,并构建一般化的枢纽量.
主要成果:
- 对 $ \mu $, $ \sigma^2 $,和 $ \beta $ 获得了准确的置信区间.
- 对于 $ \eta $,可靠性函数,生命周期的 $ p $ th 百分位数和平均时间到故障,推导出了通用化的置信区间.
- 模拟研究证实了在覆盖率百分比方面提出的一般化置信区间的有效性.
结论:
- 一般化的枢纽数量方法有效地模拟了具有降解和突然故障模式的竞争性故障.
- 导出的置信区间为关键可靠性指标提供可靠的估计.
- 拟议的方法为复杂系统的可靠性分析提供了一种实用的方法.
相关概念视频
Parametric Survival Analysis: Weibull and Exponential Methods
429
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
429
Assumptions of Survival Analysis
126
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
126
Survival Curves
151
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
151
Kaplan-Meier Approach
137
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
137
Introduction To Survival Analysis
235
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
235
Wald-Wolfowitz Runs Test II
239
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
239


