失效率的综合E-Bayes估计

该文提出了可靠性参数的一种新估计方法综合E-Bayes估计法.在无失效数据情形下给出了失效率的E-Bayes估计的定义,并给出了失效率的E-Bayes估计。在引进失效信息后，给出了失效率的E-Bayes估计，并在此基础上给出了失效率和其它参数的综合E-Bayes估计。最后，结合实际问题进行计算，结果表明该文提出的方法可行且便于应用。     

两比例失效率元件组成串联系统元件冗余与系统冗余的随机比较

针对两个比例失效率元件组成的串联系统,在热冗余的情形下,讨论了串联系统的元件冗余与系统冗余两种方案,并基于随机序的方法,对普通随机序、失效率序、反失效率序建立了元件冗余优于系统冗余的随机比较理论.     

关于反失效率序,DRHR和IEIT寿命分布类的几个结论

在这篇论文中,首先提出了一个基于期望休止时间序的反失效率序的等价刻画,并且利用膨胀序给出了递增期望休止时间分布类的一个新的刻画.然后,研究了当元件属于某个寿命分布类时所揭示的k-out-t-n系统的休止时间的变化情况,以及同一K-out-t-n系统不同的休止时间变化情况.类似的,考虑了在k-out-t-n系统中最先失效元件的休止时间的情况.     

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.     

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.     

关于元件失效率等级鉴定试验的一种贝叶斯方法

为鉴定元件失效率的等级,按目前国家标准GB1772-79规定的抽样方案,需要做大量的试验,构成人力、物力的浪费,做较高等级的鉴定甚至是不可能的.本文采用贝叶斯方法,利用大量的历史数据和工程技术人员的丰富经验确定先验分布,制定出元件失效率等级鉴定试验的一种新方案.按照这一方案,在确保产品质量的前提下,将大大节省试验时间,从而得到十分可观的经济效益。     

无失效数据情形失效率的综合估计

对指数分布的无失效数据，提出了无失效数据情形失效率的综合估计法。在失效率的先验分布为截尾Gamma分布时，给出了失效率的多层Bayes估计。在引进失效信息后，在失效率的先验分布为截尾Gamma分布时，给出了失效率的多层Bayes估计和综合估计，并给出了可靠度的综合估计，结合实际问题进行了计算。     

Archimedean copula刻画的尺度比例失效率模型的极小次序统计量的随机序北大核心CSCD

在多元链式优化序下,该文研究了两组来自于不同相依尺度比例失效率分布的最小次序统计量的随机比较.在某种数学意义下,一个由尺度比例失效率分布的不同脆弱参数和尺度参数构成的矩阵变化到另一个矩阵时,该文研究了在一定的条件下,来自于第一个尺度比例失效率分布的最小次序统计量在普通随机序下小于变化到的参数矩阵对应的尺度比例失效率分布的最小次序统计量.该文也给出了一些数值例子来说明得到的结果的正确性.     

一个新的具有单调降失效率的寿命分布

本文由Pareto分布和Logarithmic分布"混合"生成两参数具有单调降失效率的新型寿命分布,研究了该分布的矩、熵、失效率函数、平均剩余寿命和参数的极大似然估计,应用EM算法求参数的极大似然估计,进行了数值模拟.     

混合模型中失效率函数的性质与混合分布族

在失效数据的Ｃｏｘ混合模型中混合后的失效率函数的性质会与基准失效率函数的性质有很大的不同。一个非常重要的现象是：在一些常用的混合分布下,当基准失效率为上升函数时,混合后的失效率却可能为下降函数。本文在Ｃｏｘ混合模型下讨论混合后的失效率函数的增减性质与基准失效率及混合分布的关系。在此基础上推荐一个参数族－平移共轭稳定分布族,作为混合分布族。此分析族包含一个熟知的分布,用它作混合分布族可以拟合具有不同     

无失效数据情形失效率的综合E-Bayes估计

给出了无失效数据情形失效率的E-Bayes估计和引进失效信息后的E-Bayes估计,并在此基础上给出了失效率和可靠度的综合E-Bayes估计。最后,结合实际问题进行了计算,对估计结果进行了分析,并将它们与未假定先验信息时的非Bayes置信限进行比较,结果表明该方法是合理的。     

无失效数据失效率的综合多层Bayes估计

文章对指数分布无失效数据的失效率,在先验分布为Ｇａｍｍａ分布时,在引进失效信息后,给出了多层Ｂａｙｅｓ估计以及综合多层Ｂａｙｅｓ估计,并给出了可靠度的综合估计。最后,结合实际问题进行了计算     

一类基于故障率估计的优化试验设计方法

以先验信息为基础,在给定的总试验量下,以系统故障率估计的均方误差最小为优化目标,给出解析算法和蒙特卡洛模拟两种计算方法,确定了不同环境下试验量的最优分配方案.最后,将该优化试验设计方法应用于某型号车载速射迫炮在三种温度环境下的优化试验设计.     

Markov Decision Processes with a Constraint on the Asymptotic Failure Rate

In this paper, we introduce a Markov decision model with absorbing states and a constraint on the asymptotic failure rate. The objective is to find a stationary policy which minimizes the infinite horizon expected average cost, given that the system never fails. Using Perron-Frobenius theory of non-negative matrices and spectral analysis, we show that the problem can be reduced to a linear programming problem. Finally, we apply this method to a real problem for an aeronautical system.     

A model of imperfect preventive maintenance with dependent failure modes

Consider a system subject to two modes of failures: maintainable and non-maintainable. A failure rate function is related to each failure mode. Whenever the system fails, a minimal repair is performed. Preventive maintenances are performed at integer multiples of a fixed period. The system is replaced when a fixed number of preventive maintenances have been completed. The preventive maintenance is imperfect because it reduces the failure rate of the maintainable failures but does not affect the failure rate of the non-maintainable failures. The two failure modes are dependent in the following way: after each preventive maintenance, the failure rate of the maintainable failures depends on the total of non-maintainable failures since the installation of the system. The problem is to determine an optimal length between successive preventive maintenances and the optimal number of preventive maintenances before the system replacement that minimize the expected cost rate. Optimal preventive maintenance schedules are obtained for non-decreasing failure rates and numerical examples for power law models are given.     

一种具有“浴盆”失效率的寿命分布的性质与统计分析

给出了一种具有"浴盆"失效率的寿命分布形式,讨论了该寿命分布密度函数、失效率函数的图像特征,证明了其高阶矩的存在性,讨论了"浴盆"底部的宽度以及寿命分布中参数的含义。研究了该寿命分布参数的极大似然估计、最小二乘估计,通过Monte Carlo模拟认为极大似然估计更为精确。论文最后通过四个具体实例说明该寿命分布的合理性,具有实际应用价值。     

Failure rate of the inverse Gaussian-Weibull mixture model

Motivated by the idea that different causes of failure of a given system could lead to different failure distributions, a mixture of two-component distributions, one of which is the two-parameter Inverse Gaussian (IG) and the other the two-parameter Weibull (W), is proposed as a failure model. The IG-W mixture model convers several types of failure rates (FR's). It is shown that depending on the parameter values, the IG-W mixture model is capable of covering six different combinations of FR's, as one of the components has an upsidedown bathtub failure rate (UBTFR) or increasing failure rate (IFR) and the other component has a decreasing failure rate (DFR), constant failure rate (CFR), or IFR. A study is made for the mixed FR based on these six combinations.     

有优先权且工作故障与贮备故障后的修理时间不相同的温贮备系统的可靠性分析

为了解决开关寿命为连续随机变量且部件工作故障的修理时间与贮备故障后的修理时间各不相同的问题,利用Markov过程理论和Laplace变换方法,研究了有优先权的两不同型部件和两不同修理工组成的温贮备可修系统.假定部件的工作寿命、贮备寿命、工作故障的修理时间和贮备故障的修理时间均服从不同的指数分布,得到了该系统的可靠度Laplace变换和系统的首次故障前平均时间的解析表达式.     

Calibrating software reliability models when the test environment does not match the user environment

Software failures have become the major factor that brings the system down or causes a degradation in the quality of service. For many applications, estimating the software failure rate from a user's perspective helps the development team evaluate the reliability of the software and determine the release time properly. Traditionally, software reliability growth models are applied to system test data with the hope of estimating the software failure rate in the field. Given the aggressive nature by which the software is exercised during system test, as well as unavoidable differences between the test environment and the field environment, the resulting estimate of the failure rate will not typically reflect the user‐perceived failure rate in the field. The goal of this work is to quantify the mismatch between the system test environment and the field environment. A calibration factor is proposed to map the failure rate estimated from the system test data to the failure rate that will be observed in the field. Non‐homogeneous Poisson process models are utilized to estimate the software failure rate in both the system test phase and the field. For projects that have only system test data, use of the calibration factor provides an estimate of the field failure rate that would otherwise be unavailable. For projects that have both system test data and previous field data, the calibration factor can be explicitly evaluated and used to estimate the field failure rate of future releases as their system test data becomes available. Copyright © 2002 John Wiley & Sons, Ltd.