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Optimization of reliability design problems considering uncertainty in component reliability and time-to-failure.

机译：考虑组件可靠性和故障时间不确定性的可靠性设计问题的优化。

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摘要

Optimization algorithms were developed to solve complex reliability design problems which explicitly consider uncertainty in component reliability and time-to-failure. The developed algorithms allow for the determination of design solutions for very complex and difficult problems, which previously could not have been solved. Additionally, the research is very practical and will provide sophisticated design optimization methods which can be used for many new and evolving engineering designs when there is uncertainty in component reliability values. This research is characterized by a fundamentally different and improved perspective of the reliability optimization process.; The objective of the redundancy allocation problem is to determine a design configuration and to select components which collectively optimize some objective function (usually maximization of system reliability or minimization of system cost) without violating system-level constraints. The problems were formulated in a more general framework than had previously been accomplished, allowing for variability in component reliability and time-to-failure. Component and system reliability were considered as random variables and procedures were developed to accurately estimate a lower bound of system reliability based only on the means and variances of component reliability values. Since variability of system reliability was considered, it was also necessary to incorporate the element of user risk; the probability that the actual system reliability will be lower than some specified value or a lower-bound estimate. The extent of risk associated with a particular system design project was included in the problem formulations and influenced the final design configurations. These formulations were intended to more faithfully address the actual concerns and considerations of the engineering design community.; A genetic algorithm (GA) was used to determine the optimal design configuration and an adaptive penalty function was developed to satisfy constraints. The GA was tested on many different variations of the redundancy allocation problem with excellent results. The GA proved to be very effective in terms of solution quality, robustness, repeatability and computational effort.
机译：开发了优化算法来解决复杂的可靠性设计问题，这些问题明确考虑了组件可靠性和故障时间的不确定性。所开发的算法可以确定非常复杂和困难的问题的设计解决方案，而这些问题以前是无法解决的。此外，这项研究非常实用，将提供复杂的设计优化方法，当组件可靠性值不确定时，这些方法可用于许多新的和不断发展的工程设计。该研究的特点是对可靠性优化过程有根本不同的改进观点。冗余分配问题的目的是确定设计配置并选择在不违反系统级约束的情况下共同优化某个目标功能（通常是使系统可靠性最大化或使系统成本最小化）的组件。这些问题是在比以前更全面的框架中提出的，从而允许组件可靠性和故障时间的差异。组件和系统可靠性被视为随机变量，开发了仅基于组件可靠性值的均值和方差来准确估计系统可靠性下限的过程。由于考虑了系统可靠性的可变性，因此也有必要纳入用户风险要素。实际系统可靠性低于某个特定值或较低估计值的概率。与特定系统设计项目相关的风险程度已包含在问题公式中，并影响了最终的设计配置。这些表述旨在更忠实地解决工程设计界的实际关注和考虑。遗传算法（GA）用于确定最佳设计配置，并开发了一种自适应惩罚函数来满足约束条件。遗传算法在冗余分配问题的许多不同变体上进行了测试，并获得了出色的结果。事实证明，GA在解决方案质量，鲁棒性，可重复性和计算量方面非常有效。

著录项

  • 作者

    Coit, David William.;

  • 作者单位

    University of Pittsburgh.;

  • 授予单位 University of Pittsburgh.;
  • 学科 Engineering Industrial.; Operations Research.
  • 学位 Ph.D.
  • 年度 1996
  • 页码 198 p.
  • 总页数 198
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;运筹学;
  • 关键词

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