无失效数据情形可靠性参数的估计和调整

本文在无失效取样情形下,提出了产品可靠性参数的一种估计和调整的方法———加权多层Bayes估计法.在无失效数据情形下失效率的多层Bayes估计和引进失效信息后失效率的多层Bayes估计的基础上,对可靠性参数进行了估计和调整———给出了失效率和可靠度的加权多层Bayes估计.最后,结合发动机的实际问题进行了计算,结果表明本文提出的方法可行且便于应用.     

Weibull分布场合无失效数据的可靠性验证试验

对于Weibull分布的无失效数据问题,利用Bayes方法给出了产品寿命服从Weibull分布,形状参数的先验分布为U(0,1),尺度参数为1,假定产品的可靠性指标达到某个给定的值的情况下,无失效数据的可靠性验证试验,并利用相同的分析方法给出形状参数的Bayes估计.     

长寿命产品在小子样缺失数据下的Bayes可靠性增长分析

在长寿命产品的可靠性增长试验过程中,由于人员、观测设备或其他方面的原因,可能会造成某些试验数据丢失或未观测到的现象。对这类小子样变总体缺失数据情形,提出了Bayes可靠性增长分析方法。首先利用Box-Tiao技术构造先验分布,然后利用非齐次Poisson过程原理和缺失数据的产生机制,得到可靠性增长缺失数据的似然函数,再用Bayes统计推断方法得到产品各研制阶段结束时的可靠性水平,同时给出了缺失数据下增长模型的拟合优度检验方法。最后通过一个示例说明了该方法在工程上的应用。     

无失效数据情形指数分布失效率的E-Bayes估计

提出了一种新的可靠性参数估计方法——E-Bayes估计法.对寿命服从指数分布的产品,在无失效数据情形给出失效率的E-Bayes估计的定义、E-Bayes估计,并在此基础上给出了E-Bayes估计的性质——可靠性参数的E-Bayes估计和多层Bayes估计的关系.最后,结合发动机的实际问题进行了计算,结果表明提出的方法可行且便于应用.     

失效率的E-Bayes估计和多层Bayes估计

提出了一种可靠性参数的估计方法—E-Bayes估计法,对寿命服从指数分布的产品,在无失效数据情形,给出了失效率的E-Bayes估计的定义、E-Bayes估计和多层Bayes估计,并在此基础上给出了E-Bayes估计的性质.最后,结合发动机的实际问题进行了计算,结果表明E-Bayes估计法可行且便于应用.     

供应链网络可靠性的多层Bayes估计模型

对供应链网络可靠性进行界定和分析,以节点企业间的协同为基础,得到可靠度计算模型,以此为依据采集实证样本无失效运行的数据.根据供应链网络可靠性统计特性,建立一种多层Bayes估计方法,应用于样本可靠性评估中.在估计失效率的基础上,对供应链网络可靠度进行估计,对仿真结果进行分析,显示多层Bayes估计方法应用效果较好,精确度高,反映了参数不确定性对供应链网络可靠性的影响,能够较好地解决依据无失效数据判定供应链网络可靠性水平的问题.     

09-0685(四次修改稿)_王亮 师义民 (逐步增加II型截尾下比例危险率模型的可靠性分析) 2010-03-05

基于逐步增加 II 型截尾样本，分别在均方损失和 Linex 损失下，利用 ML-II 方法研究了比例危险率模型的参数和可靠性指标的经验 Bayes 估计问题。为了研究估计结果的精确性，分析了一个实际应用例子，并利用 Monte-Carlo 方法给出一个数值模拟例子，结果表明在非对称 Linex 损失下，经验 Bayes 估计更具灵活性，且结果更加有效。     

具有测量误差的非线性模型的Bayes估计

测量中大量的函数模型都是非线性回归模型．当回归变量含有一定的测量误差时，我们得到非线性测量误差模型．本讨论了这种模型中未知参数具有正态先验分布时的参数Bayes估计方法，并对这种估计进行了影响分析，证明了删除模型与均值漂移模型中参数的Bayes估计相同，利用Cook统计量给出了删除模型下参数的Bayes估计的影响度量．     

双参数指数分布环境因子的Bayes估计

讨论了定数截尾样本下双参数指数分布环境因子的极大似然估计、区间估计和Bayes估计.以参数后验密度的商密度作为环境因子的后验密度,并结合专家经验运用Bayes方法给出了环境因子在平方损失下和LINEX损失下的Bayes估计.最后运用Monte Carlo方法对各估计结果的均方误差(MSE),进行了模拟比较.结果表明LINEX损失下环境因子的估计较好.     

加权先验下的Bayes方差估计

针对具有多阶段试验信息的参数估计问题,考虑到历史数据和现场数据的不可等同性,给基于历史数据的似然函数加一个权参数a0(0≤a0≤1)的方案,结合动态修正Bayes估计方法,建构并推导出正态分布方差的加权先验Bayes估计模型.     

逐步区间删失Weibull分布的Bayesian稳健设计

逐步区间删失是获取高可靠性产品相关信息的一种重要方法.文章研究了产品寿命服从Weibull分布,带有随机移除逐步区间删失寿命试验的最优设计问题.采用极大似然方法获取模型参数的估计及其信息矩阵.利用Bayesian方法处理模型参数未知情况下设计准则对模型参数的依赖问题,获得了模型参数估计的Bayesian稳健设计准则.在考虑试验费用有限制的条件下,给出了获得最优稳健设计非线性混合整数算法.同时对先验选取及约束参数设定的敏感性做了分析.数值结果表明文章提出的方法是有效可行的.     

一个贝叶斯离散可靠性增长模型(英)

本文提出了一个贝叶斯离散可靠性增长模型．假设一个产品的开发过程由m个阶段组成．在每一个阶段中，都进行一个成败型寿命试验．在试验结束后，再分析其结果，然后对产品进行修改或重新设计，以期提高产品的可靠性．如果产品的失效可分为不可修复的以及可修复的两种．假定产品的不可修复失效概率在各个阶段中保持相同，而可修复失效概率随着试验阶段的增加而减少．     

治愈率模型在屏蔽数据可靠性分析中的应用研究

针对传统方法中的不足,在引入标准治愈率模型的基础上,提出在屏蔽数据可靠性分析中应用一种扩展的治愈率模型的建模方法;分析证明了利用该方法进行建模分析时仅需对模型作较少的前提假设,在信息不足的情况下能够识别出伴随变量对系统寿命分布的影响,进而有效提高模型估计的稳健性.通过运用基于Gibbs抽样的MCMC方法动态模拟出相关参数后验分布的马尔可夫链,给出随机截尾条件下模型参数的贝叶斯估计;实例分析的结果,证明了该模型在可靠性应用中的直观性与有效性.     

Bayesian estimation for generalized exponential distribution based on progressive type-I interval censoring

In this study, we consider the Bayesian estimation of unknown parameters and reliability function of the generalized exponential distribution based on progressive type-I interval censoring. The Bayesian estimates of parameters and reliability function cannot be obtained as explicit forms by applying squared error loss and Linex loss functions, respectively; thus, we present the Lindley’s approximation to discuss these estimations. Then, the Bayesian estimates are compared with the maximum likelihood estimates by using the Monte Carlo simulations.     

基于MCMC模拟方法的带变点的NHPP软件可靠性模型

本文主要讨论软件测试过程中NHPP模型参数发生变化的情形,并用Bayes方法对GGO模型进行变点分析,运用基于Gibbs抽样的MCMC方法模拟出参数后验分布的马尔科夫链,最后借助于BUGS软件包对软件故障数据集Musa进行建模仿真,其结果表明该模型在软件可靠性变点分析中的直观性和有效性。     

Bayesian Influence Assessment in the Growth Curve Model with Unstructured Covariance

From a Bayesian point of view, in this paper we discuss the influence of a subset of observations on the posterior distributions of parameters in a growth curve model with unstructured covariance. The measure used to assess the influence is based on a Bayesian entropy, namely Kullback-Leibler divergence (KLD). Several new properties of the Bayesian entropy are studied, and analytically closed forms of the KLD measurement both for the matrix-variate normal distribution and the Wishart distribution are established. In the growth curve model, the KLD measurements for all combinations of the parameters are also studied. For illustration, a practical data set is analyzed using the proposed approach, which shows that the diagnostics measurements are useful in practice.     

Reliability tests for Weibull distribution with variational shape parameter based on sudden death lifetime data

In the traditional design of reliability tests for assuring the mean time to failure (MTTF) in Weibull distribution with shape and scale parameters, it has been assumed that the shape parameter in the acceptable and rejectable populations is the same fixed number. For the purpose of expanding applicability of the reliability testing, Hisada and Arizono have developed a reliability sampling scheme for assuring MTTF in the Weibull distribution under the conditions that shape parameters in the both populations do not necessarily coincide, and are specified as interval values, respectively. Then, their reliability test is designed using the complete lifetime data. In general, the reliability testing based on the complete lifetime data requires the long testing time. As a consequence, the testing cost becomes sometimes expensive. In this paper, for the purpose of an economical plan of the reliability test, we consider the sudden death procedure for assuring MTTF in Weibull distribution with variational shape parameter.     

Reliability analysis for Weibull distribution with homogeneous heavily censored data based on Bayesian and least-squares methods

The reliability for Weibull distribution with homogeneous heavily censored data is analyzed in this study. The universal model of heavily censored data and existing methods, including maximum likelihood, least-squares, E-Bayesian estimation, and hierarchical Bayesian methods, are introduced. An improved method is proposed based on Bayesian inference and least-squares method. In this method, the Bayes estimations of failure probabilities are focused on for all the samples. The conjugate prior distribution of failure probability is set, and an optimization model is developed by maximizing the information entropy of prior distribution to determine the hyper-parameters. By integrating the likelihood function, the posterior distribution of failure probability is then derived to yield the Bayes estimation of failure probability. The estimations of reliability parameters are obtained by fitting distribution curve using least-squares method. The four existing methods are compared with the proposed method in terms of applicability, precision, efficiency, robustness, and simplicity. Specifically, the closed form expressions concerning E-Bayesian estimation and hierarchical Bayesian methods are derived and used. The comparisons demonstrate that the improved method is superior. Finally, three illustrative examples are presented to show the application of the proposed method.     

BAYESIAN ANALYSIS OF DATA WITH ONLY ONE FAILURE

The hearings of a certain type have their lives following a Weibull distribution. In a life test with 20 sets of bearings, only one set failed within the specified time, and none of the remainder failed even after the time of to estimate the reliabilWith a set of testing data like that in Table 1, it is required to estimate the reliability at the mission time, In this paper, we first use hierarchical Bayesian method of determine the prior distribution and the Bayesian estimates of various probabilities of failures, pi‘s, then use the method of least squares to estimate the parameters of the Weibull distribution and the reliability. Actual computation shows that the estimates so obtained are rather robust. And the results have been adopted for practical use.