基于无失效数据的指数型元件及系统的可靠性置信限

设指数寿命型元件有成败型试验数据,在ti时刻随机抽取Ni个元件试验,结果无失效元件,基于试验数据（ti,Ni,Ni)(i=(1,k)^-,讨论了元件及系统的可靠性置信限。     

指数型元件的贮存可靠性的置信限

基于元件的定期检测数据,不确定数据,通过把各检测时刻的元件的试验数据,折合成ｔ时刻下元件的伪试验数据来确定元件的贮存可靠性的置信限。给出的数字例并进行了模拟计算,证实新方法效果偏好,操作方便。     

对数正态型元件贮备系统可靠性的置信下限

本文研究了由κ个相互独立的元件和一个转换开关组成的贮备系统.假设元件的寿命服从对数正态分布,转换开关为成败型,本文给出了系统可靠性的表达式、点估计以及Fiducial和Bayes置 信下限.此外,还给出了系统可靠性的点估计以及置信下限的数值计算方法.在小样本情形下, 通过数值比较研究了Fiducial置信下限和Bayes置信下限的覆盖率性质.     

考虑使用与维修优先权的冷贮备系统可靠性模型分析

针对装备系统中部件关键重要程度不同的情况,以具有使用与维修优先权的多部件冷贮备系统为研究对象,利用适用性较强的phase-type（PH）分布代替以往在可靠性建模中采取的指数分布、Weibull分布等典型分布,统一描述了系统各部件的寿命和维修时间,并建立了通用性更好的系统可靠性模型。在模型解析过程中,采用矩阵解析方法,获得了系统的可靠度函数、稳态可用度以及系统平均开工时间、平均停工时间等工程实践中常用的系统可靠性指标解析表达式,并利用算例验证了模型的适用性,讨论了贮备部件数量对系统可靠性指标的影响。研究结果表明,利用PH分布对具有使用和维修优先权的冷贮备系统进行可靠性解析建模,不仅具有良好的解析特性,而且能够有效地保证模型的适用性,在工程实践中具有良好的应用价值。     

逐步增加的Ⅱ型截尾下冷贮备串联系统可靠性指标的Bayes估计

在逐步增加的Ⅱ型截尾模型下,研究部件寿命服从双参数指数分布的冷贮备串联系统可靠性指标的Bayes估计。在超参数未知的情形下,分别在平方损失(SE),LINEX损失和熵(General Entropy,GE)损失函数下给出两个参数及可靠性指标的Bayes估计;对于超参数的确定,给出一种新的方法;最后利用随机模拟方法进行比较,并对结果进行了讨论。     

基于隐蔽的系统成败型数据的元件可靠性的极大似然估计和区间估计

考虑由３个独立工作的成败型元件组成的串联系统，利用隐蔽的系统寿命试验数据求元件可靠性的极大似然估计和区间估计，给出了数值例子。     

不同二项、指数单元并联系统可靠性评定的Bayes方法

针对不同二项、指数单元并联系统的可靠性评定,详细讨论了可靠性评定的Bayes方法,通过β分布函数与Г分布函数的分位数快速计算,可以快速、准确地得到可靠性评定结果.并且对先验参数的确定方法,进行了讨论.最后给出计算机程序流程图及算例.为航天领域可靠性评定与设计提供了工程实用方法与模块.     

具有分离变量型的非线性滞后型系统解的指数稳定性

介绍了一种研究一类系统解的指数稳定性方法，并用此法讨论了二个泛函微分方程，方法直观，所得结果易于验证。     

基于寿命周期备件费用的系统可靠性优化设计

针对以往系统可靠性优化研究中,系统费用仅包括研制成本,未考虑系统在列装期间运行费用的问题,应用一种基于寿命周期内备件需求估计的备件费用简易计算方法,对两种非线性可靠性-价格函数情况,以系统组成单元不可修、寿命分布服从指数规律为例,开展了以系统价格和备件费用之和最小为目标的可靠性分配优化研究。仿真试验结果表明:该方法简便易行,得到的优化方案更加合理,能避免单纯追求系统价格最低、却在装备使用阶段付出高昂费用的过度优化结果,能有效降低系统在全寿命期间的运行成本。     

面向过电流保护元件系列产品研发的试验数据管理系统的应用

以过电流保护元件系列产品研发的试验数据管理的需求为出发点,针对试验数据易变性和专业性的特点,通过详细的系统需求分析和系统功能模块设计,建立了面向过电流保护元件系列产品研发的试验数据管理系统。该系统在企业的有效应用不但提高了试验效率,还使技术资源得到沉淀、积累和重复利用,提高了企业的核心竞争力。     

Comparison of Two Methods of Obtaining Approximate Confidence Intervals for System Reliability

Some specific comparisons are made in this note between the use of the asymptotic Chi-square distribution of the likelihood ratio and the asymptotic normality of the maximum likelihood estimates to obtain confidence interval for reliabilities of arbitrary systems when only failure data on the components is known. In all the comparisons made, using moderate samples and systems of average complexity, the asymptotic Chi-square appears to give much more accurate confidence intervals. Although the asymptotic Chi-square method requires more computation for most systems than does the method based on asymptotic normality, these examples indicate the Chi-square method would yield superior results in most practical instances.     

APProximate Confidence Limits for the Reliability of Series and Parallel Systems

Suppose a complex mechanism, e.g., a missile, is built up from a number of different types of components, where the reliability of each of the components has been estimated by means of separate tests on each of the components. This paper gives a method for combining such data to determine approximate confidence limits for the reliability of the complete mechanism. More precisely, a method of determining approximate confidence limits for the reliability of “series,” “parallel,” and “seriesparallel” systems is given, based on observed failures of the individual components. It is assumed that the failures are independent, and that failures of a given component follow a binomial distribution with unknown parameter, the component reliability. The large-sample properties of the likelihood-ratio test are then used to construct the appropriate confidence limits for the system reliability.     

Approximate Fiducial Bounds for the Reliability of a Series System for Which Each Component has an Exponential Time-to-Fail Distribution

The computation of exact confidence limits on the reliability of systems using sample data on components turns out to be a very involved process or one requiring very extensive Monte Carlo runs on computers. We suggest here a simple, straight-forward procedure for approximating the fiducial probability bounds on the true system reliability which may be used as approximations to confidence bounds for the reliability of a series system for which each component has an exponential time-to-fail distribution.     

Analysis of Confidence Lower Limits of Reliability and Hazard Rate for Electronic Stability Control Systems

This article describes an evaluation method for reliability and hazard rate of lifetime distribution confidence lower limits for electronic stability control (ESC) system using test data from multistage and subsystems. This method provides estimation that can potentially and significantly reduce the amount of testing, without sacrificing the one‐sided confidence level of reliability. This also allows quicker design verification and validation for ESC systems. The method is derived under the assumption that the reliability parameter is a random variable with a given distribution function, and the product's reliability increases monotonously during the development process. This new method is applied to the analysis of reliability of an ESC system. Copyright © 2012 John Wiley & Sons, Ltd.     

Bootstrap Confidence Limits for Short-Run Capability Indices

The capability indices are widely used by quality professionals as an estimate of process capability. Recently, techniques and tables were developed to construct confidence limits for each index. These techniques are based on the assumption that the underlying process is normally distributed. In industries it may not be possible to get large samples, and, hence, the normality assumption is often violated. For the short-run production processes where sample size is small, appropriate indices can be developed. In this article, the capability indices such as C_p, C_pk, and C_pm are modified, and appropriate capability indices are constructed. A further bootstrap technique is used to define the confidence intervals. A simulation using two distributions (one normal and the other nonnormal) is conducted, and a comparison is made to show the performances of the three nonparametric confidence intervals.     

Approximate Confidence Intervals for the Log‐Normal Standard Deviation

In this work, approximate confidence intervals are derived for the standard deviation of a log‐normal distribution. Simulations are conducted to evaluate the coverage probability, average length, and coverage bias of the derived approximate confidence intervals. The simulation results indicate that the proposed approximate confidence intervals perform reasonably well when the standard deviation of the log‐transformed variable is small. The approximate confidence intervals are applied to a phase I pharmacokinetic study and a real data set concerning a lab testing of reference engine oil. Copyright © 2015 John Wiley & Sons, Ltd.