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．     

水雷保险器水压膜贮存寿命预测

本文以水雷保险器水压膜贮存失效数据为研究对象,建立了水压膜贮存统计模型,采用周期测量数据失效时刻划分的方法,将不完全失效数据转换为区间数据;提出了基于对数正态分布族和基于极值分布族的水压膜贮存寿命预测方法,对水雷其它零部件的寿命预测具有一定的参考价值。     

Optimal apogee burn time for low thrust spinning satellite in low altitude

In this study, the optimal burn time for low-thrust impulsive propulsion systems is investigated to raise the perigee altitude of a low-Earth orbit. The maneuver is done using spin-stabilized attitude control and impulsive thrusting system for a time interval centered about apogee point. On the one hand, the low value of the thrust level causes more burn time needed to accomplish the transfer. This, in turn, will cause more thrust loss due to the deviation between the thrust axis (spin axis) and the velocity vector of the satellite. On the other hand, for small thrust duration, the transfer needs more revolutions around the Earth and more travel in lower altitudes with dense atmosphere and more drag loss. To transfer the satellite with minimum propellant mass, a compromise between velocity losses due to both drag and thrust deviation angle should be made. An analytical approximate correlation between average thruster burn time and total required propellant mass is formulated in this study and an analytical optimal solution for burn time is found. Nonlinear programming is used to find optimal burn time history. Comparing the analytical and numerical results shows a very good match.     

基于测试数据的装备贮存可靠性评估

针对无失效数据情形下装备贮存可靠性估计问题,提出了一种利用性能测试数据进行估计的方法.首先利用测试数据估计装备在不同测试时的失效概率,然后利用配分布曲线法估计装备贮存寿命分布函数中的未知参数.由于方法充分利用了装备性能测试数据中所隐含的可靠性变化趋势,使其估计结果具有一定的可信性.     

复杂系统平均寿命综合评估方法研究

提出了一种利用复杂系统组成设备的试验信息,对复杂系统平均寿命进行综合评估的方法.同时还给出了设备级试验数据具有删失情况下的数据转化方法及设备级可靠度的评估方法.最后,通过计算机模拟对所提供方法进行了验证.     

Planning accelerated life tests with random effects of test chambers

In accelerated life tests (ALTs), test units are often tested in multiple test chambers along with different stress conditions. The nonhomogeneity of test chambers precludes the complete randomized experiment and may affect the life‐stress relationship of the test product. The chamber‐to‐chamber variation should be taken into account for ALT planning so as to obtain more accurate test results. In this paper, planning ALTs under a nested experimental design structure with random test chamber effects is studied. First, by a 2‐phase approach, we illustrate to what extent different test chamber assignments to stress conditions may impact the estimation of unknown parameters. Then, D‐optimal test plans with 2 test chambers are considered. To construct the optimal design, we establish the generalized linear mixed model for failure‐time data and apply a quasi‐likelihood method, where test chamber assignments, as well as other decision variables that are required for planning ALTs, are simultaneously determined.     

军事行动避空侦察的时机和路线选择

介绍了军事行动中侦察卫星运动轨迹的预测方法,并规划了最优行军路线.首先根据雷达探测俯仰角与探测距离的几何关系,确定雷达探测经度范围,并对卫星周期及卫星被探测到的初始位置进行求解,从而建立卫星进出雷达探测范围时刻的预测模型,对几种卫星的过顶情况进行预测·最后,在此基础上建立简化的行军路线模型,选择最优行军路线,躲避卫星侦察.     

Testing for increasing convex order in several populations

Increasing convex order is one of important stochastic orderings. It is very often used in queueing theory, reliability, operations research and economics. This paper is devoted to studying the likelihood ratio test for increasing convex order in several populations against an unrestricted alternative. We derive the null asympotic distribution of the likelihood ratio test statistic, which is precisely the chi-bar-squared distribution. The methodology for computing critical values for the test is also discussed. The test is applied to an example involving data for survival time for carcinoma of the oropharynx.     

The reliability analysis of probabilistic and interval hybrid structural system

The aim of this paper is to evaluate the reliability of probabilistic and interval hybrid structural system. The hybrid structural system includes two kinds of uncertain parameters—probabilistic parameters and interval parameters. Based on the interval reliability model and probabilistic operation, a new probabilistic and interval hybrid reliability model is proposed. Firstly, we use the interval reliability model to analyze the performance function, and then sum up reliability of all regions divided by the failure plane. Based on the presented optimal criterion enumerating the main failure modes of hybrid structural system and the relationship of failure modes, the reliability of structure system can be obtained. By means of the numerical examples, the hybrid reliability model and the traditional probabilistic reliability model are critically contrasted. The results indicate the presented reliability model is more suitable for analysis and design of these structural systems and it can ensure the security of system well, and it only needs less uncertain information.     

New maximum entropy-based algorithm for structural design optimization

A new algorithm based on nonlinear transformation is proposed to improve the classical maximum entropy method and solve practical problems of reliability analysis. There are three steps in the new algorithm. Firstly, the performance function of reliability analysis is normalized, dividing by its value when each input is the mean value of the corresponding random variable. Then the nonlinear transformation of such normalized performance function is completed by using a monotonic nonlinear function with an adjustable parameter. Finally, the predictions of probability density function and/or the failure probability in reliability analysis are achieved by looking the result of transformation as a new form of performance function in the classical procedure of maximum entropy method in which the statistic moments are given through the univariate dimension reduction method. In the proposed method, the uncontrollable error of integration on the infinite interval is removed by transforming it into a bounded one. Three typical nonlinear transformation functions are studied and compared in the numerical examples. Comparing with results from Monte Carlo simulation, it is found that a proper choice of the adjustable parameter can lead to a better result of the prediction of failure probability. It is confirmed in the examples that result from the proposed method with the arctangent transformation function is better than the other transformation functions. The error of prediction of failure probability is controllable if the adjustable parameter is chosen in a given interval, but the suggested value of the adjustable parameter can only be given empirically.     

某型航空火控系统可靠度置信下限的确定

依据 AMSAA模型利用某型航空火控系统各组成单元的研制阶段试验数据并基于遗传算法确定出单元的时间环境折合系数 ,进而求得各单元 MTBF点估计和置信下限 ,最后利用 L-M法给出该系统可靠度置信下限值 .研究结果表明 ,由此得到的系统可靠性评估结果与依据设计定型后实际使用数据统计得到的可靠性水平相接近 ,应用该方法进行系统可靠性评估能较好地满足工程上的需求 .     

A bayesian approach to inference for monotone failure rates

In reliability theory, the notion of monotone failure rates plays a central role. When prior information indicates that such monotonicity is meaningful, it must be incorporated into the prior distribution whenever inference about the failure rates needs to be made. In this paper we show how this can be done in a straightforward and intuitively pleasing manner. The time interval is partitioned into subintervals of equal width and the number of failures and censoring in each interval is recorded. By defining a Dirichlet as the joint prior distribution for the forward or the backward differences of the conditional probabilities of survival in each interval, we find that the monotonicity is presenved in the posterior estimate of the failure rates. A posterior estimate of the survival function can also be obtained. We illustrate our method by applying it to some real life medical data.     

Nonparametric predictive reliability of series of voting systems

Nonparametric Predictive Inference (NPI) for system reliability reflects the dependence of reliabilities of similar components due to limited knowledge from testing. NPI has recently been presented for reliability of a single voting system consisting of multiple types of components. The components are all assumed to play the same role within the system, but with regard to their reliability components of different types are assumed to be independent. The information from tests is available per type of component. This paper presents NPI for systems with subsystems in a series structure, where all subsystems are voting systems and components of the same type can be in different subsystems. As NPI uses only few modelling assumptions, system reliability is quantified by lower and upper probabilities, reflecting the limited information in the test data. The results are illustrated by examples, which also illustrate important aspects of redundancy and diversity for system reliability.     

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Solar cell is the basic component of satellite photovoltaic panels with complicated redundant system structure. Its reliability plays an important role in the system, and its performance shows a degradation trend over time. In this paper, study is conducted for the solar cell degradation modeling and reliability analysis based on practical testing results. Specifically, we illustrate an accelerated test for the attenuation ratio character test under different accumulative irradiation levels, focusing on the heteroscedasticity of the collected testing data. A heteroscedastic linear model is proposed, and the life distribution of the photovoltaic panel is obtained by using Fiducial method. A numerical example is shown for the purpose of illustration.     

Optimum Burn-in Time for a Bathtub Shaped Failure Distribution

An important problem in reliability is to define and estimate the optimal burn-in time. For bathtub shaped failure-rate lifetime distributions, the optimal burn-in time is frequently defined as the point where the corresponding mean residual life function achieves its maximum. For this point, we construct an empirical estimator and develop the corresponding statistical inferential theory. Theoretical results are accompanied with simulation studies and applications to real data. Furthermore, we develop a statistical inferential theory for the difference between the minimum point of the corresponding failure rate function and the aforementioned maximum point of the mean residual life function. The difference measures the length of the time interval after the optimal burn-in time during which the failure rate function continues to decrease and thus the burn-in process can be stopped.      

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.     

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.     

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As a new reliability test plan, generalized progressive hybrid censoring can improve test efficiency by allowing experimenters to observe a pre-specified number of failure samples before the final termination point. Based on a class of widely used life distribution in life data analysis --- generalized exponential distribution, this paper discusses its parameters inference issue under generalized progressive hybrid censoring scheme. EM Algorithm is used to estimate parameters of the considered model. Simulation studies and a real-data analysis are carried out to illustrate the performance of finite sample for the proposed procedure.     

对地观测卫星过顶预报与定轨模型的建立

针对某些对地观测卫星,其轨道参数不公开,只能通过对卫星的运行规律建立数学模型,推导出卫星的过顶时间,从而使对地目标有效规避卫星的对地观测.首先从卫星运行的基本规律出发,根据稀疏数据进行定轨建立了卫星定轨的数学模型,利用牛顿迭代法递推出轨道根数,同时利用摄动补偿优化了模型,再通过八阶龙格库塔法,进行轨道递推,得到了卫星过顶时间与轨道信息的对应关系.     

Efficient strategy for reliability-based optimization design of multidisciplinary coupled system with interval parameters

Non-probabilistic reliability based multidisciplinary design optimization has been widely acknowledged as an advanced methodology for complex system design when the data is insufficient. In this work, the uncertainty propagation analysis method in multidisciplinary system based on subinterval theory is firstly studied to obtain the uncertain responses. Then, based on the non-probabilistic set theory, the interval reliability based multidisciplinary design optimization model is established. Considering that the gradient information of interval reliability cannot be acquired in the whole design domain, which causes convergence difficulties and prohibitive computation, an interval reliability displacement based multidisciplinary design optimization method is proposed to address the issue. In the proposed method, the interval reliability displacement is introduced to measure the degree of interval reliability. By doing so, not only the connotation of the interval reliability is guaranteed, but more importantly, the partial gradient region for interval reliability is equivalently converted into full gradient region for reliability displacement. Consequently, the gradient information can be acquired under any circumstances and thus the convergence process is highly accelerated by utilizing the gradient optimization algorithms.