具有记忆效应与单元间变异性的加速退化过程可靠性建模与统计分析
Reliability modeling and statistical analysis of accelerated degradation process with memory effects and unit-to-unit variability
AI总结：
针对加速退化中的非马尔可夫记忆效应与产品个体差异，提出融合分数布朗运动和单元间变异性的可靠性模型，并基于EM算法实现参数估计，经仿真和调谐器案例验证有效。
摘要：
对退化过程进行合理描述，对于在加速退化试验中实现可信的可靠性评估至关重要。现有方法通常采用马尔可夫随机过程来描述退化过程。然而，由于产品与环境之间的相互作用，某些产品的退化过程具有非马尔可夫性。错误理解退化模式可能导致可靠性评估出现偏差。此外，由于材料和制造工艺的差异，来自同一总体的产品会呈现出不同的退化路径，这进一步增加了准确估计可靠性的难度。为解决上述问题，本文提出一种融合记忆效应和单元间变异性的加速退化模型。退化过程中的记忆效应通过分数布朗运动（fractional Brownian motion）加以捕捉，该运动反映了退化的非马尔可夫特征。在加速模型中考虑单元间变异性，以描述多样化的退化路径。随后，给出了正常运行条件下的寿命和可靠性。进一步地，为准确估计记忆效应，设计了一种基于期望最大化算法（expectation maximization algorithm）的新型统计分析方法。所提方法的有效性通过一个仿真案例和一个真实调谐器可靠性分析案例得到验证。仿真案例的代码公开发布于 https://github.com/dirge1/FBM_ADT。
英文摘要：
A reasonable description of the degradation process is essential for credible reliability assessment in accelerated degradation testing. Existing methods usually use Markovian stochastic processes to describe the degradation process. However, degradation processes of some products are non-Markovian due to the interaction with environments. Misinterpretation of the degradation pattern may lead to biased reliability evaluations. Besides, owing to the differences in materials and manufacturing processes, products from the same population exhibit diverse degradation paths, further increasing the difficulty of accurate reliability estimation. To address the above issues, this paper proposes an accelerated degradation model incorporating memory effects and unit-to-unit variability. The memory effect in the degradation process is captured by the fractional Brownian motion, which reflects the non-Markovian characteristic of degradation. The unit-to-unit variability is considered in the acceleration model to describe diverse degradation paths. Then, lifetime and reliability under normal operating conditions are presented. Furthermore, to give an accurate estimation of the memory effect, a new statistical analysis method based on the expectation maximization algorithm is devised. The effectiveness of the proposed method is verified by a simulation case and a real-world tuner reliability analysis case. The code for the simulation case is publicly available at https://github.com/dirge1/FBM_ADT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
