基于M-最佳性标准的逆高斯过程的阶段应力加速降解试验
1Department of Statistics, School of Mathematics, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China. tangjiayin@swjtu.edu.cn.
Scientific reports
|August 25, 2025
概括
本研究引入了步骤应力加速降解试验 (SSADT) 的M-最佳性,以提高故障机制的等效性. 这种新方法将准确性与可靠性工程中故障模式的更好理解相平衡.
科学领域:
- 可靠性工程
- 统计模型
- 加速测试
背景情况:
- 步骤应力加速降解试验 (SSADT) 对于评估产品在应力条件下的可靠性至关重要.
- 优化SSADT的设计对于准确的故障分析越来越重要.
- 现有的方法往往优先考虑预测或评估准确性,而不是机制理解.
研究的目的:
- 引入和评估设计SSADT的M最佳性标准.
- 提高可靠性评估中的故障机制等效性.
- 将M-最佳性标准应用于反向高斯式 (IG) 降解过程.
主要方法:
- 在SSADT设计中应用M最佳性标准.
- 最小化机制等效因子 (Mef) 的非对称差异.
- 分析电气连接器的应力放松故障数据.
主要成果:
- 对IG过程的SSADT设计应用了M-最佳性标准.
- 已经证明M-optimality可以提高失效机制的等效性.
- 该标准对模型参数估计和寿命预测准确度的影响最小.
结论:
- 通过专注于故障机制等效性,M-优化为SSADT设计提供了一种优越的方法.
- 该标准为可靠性工程中的传统优化方法提供了有价值的替代方案.
- 该研究使用现实世界的电气连接器数据验证了M-optimality的有效性.
相关概念视频
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Propagation of Uncertainty from Random Error
1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Quantifying and Rejecting Outliers: The Grubbs Test
2.0K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.0K
Routh-Hurwitz Criterion II
402
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
402
Routh-Hurwitz Criterion I
333
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
333
Parametric Survival Analysis: Weibull and Exponential Methods
602
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...
602


