具有不确定数据的成败型元件和系统可靠性置信下限的精确解

当试验次数为ｎ，成功次数介于Ｘ１，Ｘ２时（Ｘ１≤Ｘ２）。本文给出了在这种不确定数据下成败型元件的可靠性Ｐ的极大似然估计和精确置下限，对于由成败型元件组成的串联和并联系统，本文也给出了在不确定数据下，系统可靠性的精确解。最后讨论了极大似然估计的某些性质。     

基于成败型元件试验数据和系统试验数据的串联系统可靠性的置信下限

本文根据成败型元件和系统两者的试验数据来估计串联系统可靠性的置信下限。首先根据元件和系统两者的试验数据,求出系统可靠性的极大似然估计,然后按L-M(Lindstrom-Madden)法给出系统可靠性置信下限的近似值,并提供了数字例子。     

N个部件冷贮备可靠性模型的扩散估计界

本文对Ｎ个同型部件冷贮备的可靠性模型首次提出了一类扩散估计的近似算法理论，利用这个近似算法，首先给出了系统的一个基本循回过程的向前扩散偏微分方程，然后得到了这个偏微分方程在格占 上的近似瞬态解和稳太上的近似瞬态解和稳态解，最后给出了系统稳态可靠性指标的上下估计界，算便表明这个近似算法简便易行，对大系统的可靠性研究是有效的。     

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。     

一种新的恒加试验下双参数指数型产品的可靠性分析

针对双参数指数型产品,在具有二项移走(即在每个观测时刻产品的移走数服从二项分布)的分组寿命试验下,研究了分组时刻的确定方法,推导出门限参数、寿命参数和移走概率的极大似然估计。进而,讨论了双参数指数型产品在具有二项移走的恒加寿命分组试验下的可靠性分析问题。利用加速寿命方程,给出了双参数指数型产品的可靠性估计。最后给出随机模拟例子验证了结论的正确性。     

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。     

对数正态或正态寿命型元件及系统的贮存可靠性的近似置信下限

本文给出了基于一次性检测数据的对数正态或正态寿命型元件及系统的贮存可靠性的近似置信下限的方法，并对得到的近似置信限的精度作了讨论，结果表明精度符合要求。本文还给出了数字例子。     

无失效数据的贝叶斯统计分析

本给出一类先验分布下基于失效数据的失效概率的贝叶斯和多层贝叶斯估计,证明了保序性,最后对一个无失效数据的应用例子进行了可靠性统计分析。     

基于一次性检测数据的统计分析及其在贮存可靠性中的应用

本文讨论了文（３）中极小Ｘ＾２－估计的相合性，并给出了产品可靠性指标的置信限，最后讨论了此方法在贮存可靠性中的应用。     

威布尔分布的Bayes可靠性分析

本文针对两参数的威布尔分布的元件，采用Ｂａｙｅｓ方法对其可靠性进行分析，文中分别对两种假设情况进行了讨论，第一种情况是形状参数为离散取值，尺度参数为连续取值的情况，第二种情况假设形状参数的先验分布为均匀分布，尺度参数的先验分布为逆伽玛分布，推出了对应的可靠性估计和置信下限估计，并给出了计算算法，最后用实例对算法进行了验证。     

The signal model: A model for competing risks of opportunistic maintenance

This paper presents a competing risks reliability model for a system that releases signals each time its condition deteriorates. The released signals are used to inform opportunistic maintenance. The model provides a framework for the determination of the underlying system lifetime from right-censored data, without requiring explicit assumptions about the type of censoring to be made. The parameters of the model are estimated from observational data by using maximum likelihood estimation. We illustrate the estimation process through a simulation study. The proposed signal model can be used to support decision-making in optimising preventive maintenance: at a component level, estimates of the underlying failure distribution can be used to identify the critical signal that would trigger maintenance of the individual component; at a multi-component system level, accurate estimates of the component underlying lifetimes are important when making general maintenance decisions. The benefit of good estimation from censored data, when adequate knowledge about the dependence structure is not available, may justify the additional data collection cost in cases where full signal data is not available.     

Statistical Analysis of Series System for Masked Data under Successive Censored Life Test

In this paper, considering series system of masked data under simple successive censored and multiple successive censored life test, the likelihood function and maximum likelihood estimate are respectively proposed for series system composed of two units under two kinds of situations. One is the series system composed of two units with constant failure rate, and the other is the series system composed of two units with linear failure rate through the origin. The approximate interval estimates of parameters are given by using the method of likelihood ratio. Besides, the examples show the feasibility of the methods through Monte-Carlo simulations.     

基于几何过程和位相分布休假的两部件并联可修系统的可靠性分析

主要以两不同型部件组成的并联可修系统为研究对象.在系统对失效相位存在记忆的基础上,考虑了修理工可单重休假且休假时间服从位相(PH)分布.每个工作部件均有可能因受到两种不同类型的故障而失效,且均"修复非新".在假定部件的工作时间,修理时间分别服从PH分布的几何过程和负指数分布的条件下,利用马尔可夫过程和矩阵分析的方法,对可修系统进行了可靠性分析,并给出了相应可靠性指标的数值算例.     

蒙特卡罗仿真在船用核动力装置可靠性分析中的应用

如何推断系统的故障概率,是目前可靠性工程领域的一个重要问题.而对具有动态随机性故障的可修系统采用静态近似处理,经常导致计算的可靠性指标与实际情况相差甚远,采用蒙特卡罗方法产生等价于船用核动力系统基本部件故障率的随机数,代入到仿真模型中,经过逻辑运算得到等价于系统故障概率的随机数,对多次仿真得到的数据进行统计推断,便得到系统故障的概率分布及相应的置信区间.此方法计算结果精度高,对船用核动力装置的可靠性分析有重要意义.     

Multistate components assignment problem with optimal network reliability subject to assignment budget

The typical assignment problem for finding the optimal assignment of a set of components to a set of locations in a system has been widely studied in practical applications. However, this problem mainly focuses on maximizing the total profit or minimizing the total cost without considering component’s failure. In practice, each component should be multistate due to failure, partially failure, or maintenance. That is, each component has several capacities with a probability distribution and may fail. When a set of multistate components is assigned to a system, the system can be treated as a stochastic-flow network. The network reliability is the probability that d units of homogenous commodity can be transmitted through the network successfully. The multistate components assignment problem to maximize the network reliability is never discussed. Therefore, this paper focuses on solving this problem under an assignment budget constraint, in which each component has an assignment cost. The network reliability under a components assignment can be evaluated in terms of minimal paths and state-space decomposition. Subsequently an optimization method based on genetic algorithm is proposed. The experimental results show that the proposed algorithm can be executed in a reasonable time.     

Reliability and covariance estimation of weighted k-out-of-n multi-state systems

In the literature of reliability engineering, reliability of the weighted k-out-of-n system can be calculated using component reliability based on the structure function. The calculation usually assumes that the true component reliability is completely known. However, this is not the case in practical applications. Instead, component reliability has to be estimated using empirical sample data. Uncertainty arises during this estimation process and propagates to the system level. This paper studies the propagation mechanism of estimation uncertainty through the universal generating function method. Equations of the complete solution including the unbiased system reliability estimator and the corresponding unbiased covariance estimator are derived. This is a unified approach. It can be applied to weighted k-out-of-n systems with multi-state components, to weighted k-out-of-n systems with binary components, and to simple series and parallel systems. It may also serve as building blocks to derive estimators of system reliability and uncertainty measures for more complicated systems.     

Hybrid block replacement and inspection policies for a multi-component system with heterogeneous component lives

Novel replacement policies that are hybrids of inspection maintenance and block replacement are developed for an n identical component series system in which the component parts used at successive replacements arise from a heterogeneous population. The heterogeneous nature of components implies a mixed distribution for time to failure. In these circumstances, a hybrid policy comprising two phases, an early inspection phase and a later wear-out replacement phase, may be appropriate. The policy has some similarity to burn-in maintenance. The simplest policy described is such a hybrid and comprises a block-type or periodic replacement policy with an embedded block or periodic inspection policy. We use a three state failure model, in which a component may be good, defective or failed, in order to consider inspection maintenance. Hybrid block replacement and age-based inspection, and opportunistic hybrid policies will also arise naturally in these circumstances and these are briefly investigated. For the simplest policy, an approximation is used to determine the long-run cost and the system reliability. The policies have the interesting property that the system reliability may be a maximum when the long-run cost is close to its minimum. The failure model implies that the effect of maintenance is heterogeneous. The policies themselves imply that maintenance is carried out more prudently to newer than to older systems. The maintenance of traction motor bearings on underground trains is used to illustrate the ideas in the paper.     

Random partition masking model for censored and masked competing risks data

We consider the parametric estimation with right-censored competing risks data and with masked failure cause. We propose a new model, called the random partition masking (RPM) model. The existing model based on the so called symmetry assumption, but the RPM model does not need the symmetry assumption. We propose a wide class of parametric distribution families of the failure time and cause, which does not need the assumption of independence between the components of the system. We also study the asymptotic properties of the maximum likelihood estimator under the new model, and apply our procedure to a medical and an industrial data sets.     

Covariance-Guided Mixture Probabilistic Principal Component Analysis (C-MPPCA)

To extract information from high-dimensional data efficiently, visualization tools based on data projection methods have been developed and shown useful. However, a single two-dimensional visualization is often insufficient for capturing all or most interesting structures in complex high-dimensional datasets. For this reason, Tipping and Bishop developed mixture probabilistic principal component analysis (MPPCA) that separates data into multiple groups and enables a unique projection per group; that is, one probabilistic principal component analysis (PPCA) data visualization per group. Because the group labels are assigned to observations based on their high-dimensional coordinates, MPPCA works well to reveal homoscedastic structures in data that differ spatially. In the presence of heteroscedasticity, however, MPPCA may still mask noteworthy data structures. We propose a new method called covariance-guided MPPCA (C-MPPCA) that groups subsets of observations based on covariance, not locality, and, similar to MPPCA, displays them using PPCA. PPCA projects data in the dimensions with the highest variances, thus grouping by covariance makes sense and enables some data structures to be visible that were masked originally by MPPCA. We demonstrate the performance of C-MPPCA in an extensive simulation study. We also apply C-MPPCA to a real world dataset. Supplementary materials for this article are available online.     

基于随机截尾数据的指数型元件串联系统可靠性的置信限

基于元件的随机定时截尾寿命试验数据 ,给出了指数型元件串联系统可靠性的经典精确置信下限 ,给出了数字例 .