线性异方差模型在太阳能电池退化及可靠性分析中的应用

太阳能电池片是构成卫星太阳能电池板的基本元件,太阳能电池板的可靠性及寿命是影响整个卫星可靠性和寿命的重要因素.太阳能电池片随着使用时间的延长其光电转换效率随时间发生退化.在本文中,我们基于太阳电池片的加速退化试验测试数据,对太阳能电池片的退化规律进行统计建模,并进一步对太阳能电池板进行可靠性分析与评估.针对太阳能电池片退化率与累计辐照剂量的试验数据,我们用线性异方差模型来进行建模,最后利用Fiducial推断的方法得到太阳能电池板的寿命分布,本文将利用一个实例来说明分析和计算过程.     

多应力、多失效模式的集中器可靠性评估方法

为保证集中器在温度、湿度、电应力、振动等多应力复杂环境条件下平稳运行,需要解决综合突变故障和性能退化的可靠性评估问题.因此,针对集中器的温-湿度双应力加速试验,从通信失败的突变失效和模拟量误差超差的退化失效等两种失效模式入手,综合利用AIC/BIC信息准则、退化量分布模型和Peck加速模型等方法,分别给出在不同失效模式下的可靠性评估方法;最后,基于竞争性失效模型,给出了集中器在温-湿度复杂环境下的可靠性综合评估方法.     

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.     

考虑失效相关性的长寿命系统可靠性置信限综合评估模型

根据高长寿命产品的试验数据特征,采用退化轨迹拟合、两点分布和区间估计思想,分位数置信限-失效阈值干涉、可靠度预估响应模拟,分别给出了小样本和大样本试样下的零部件可靠性置信限评估方法。运用Copula理论拟合零部件失效间的动态相关性,从而对应性地建立了系统级可靠性置信限综合评估模型。该模型改善了独立性系统可靠性置信限评估方法预估结果过于保守的情形。算例验证了该类模型的可行性和有效性。     

基于线性退化轨道的区间型建模分析及应用

目前在退化试验中,产品失效一般定义为其性能退化至低于或高于一个指定临界值(单点型退化),尽管这种定义简单实用,但却不尽合理,不能完全描述产品退化失效问题.本文改进单点型退化模型,提出区间型退化模型,并讨论了退化轨道是线性时的区间退化模型问题,获得了各种线性区间退化寿命分布.本文利用数值积分和蒙特卡罗法模拟分析对比区间退化模型和单点退化模型的寿命分布,揭示区间退化和单点退化的关系.最后通过实例分析体现区间退化在实际中更为合理有效.     

基于维纳过程步进应力加速退化试验的客观贝叶斯分析

高可靠性产品在加速寿命试验中其失效数据经常比较少,利用步进应力加速退化试验来评估产品寿命分布是一种非常好的方法.本文基于维纳过程的步进应力加速退化试验模型利用客观贝叶斯方法获得了其模型参数的无信息先验(Jefferys先验和Reference先验).并证明了对应的后验分布都是正常的.对于Jefferys先验和Reference先验下的后验提出相应的Gibbs抽样算法.最后,我们模拟对比了客观贝叶斯估计、贝叶斯估计和极大似然估计,模拟结果揭示了客观贝叶斯方法的优良性.     

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.     

费用约束下产品寿命试验与退化试验优化

针对Wiener退化失效型产品寿命试验与退化试验优化问题,以Wiener过程和逆高斯分布为依据,用分位寿命渐近方差作为可靠性指标进行了分析.首先比较了退化试验与定时截尾寿命试验的试验设计变量之间关系.然后考虑实际情况,研究试验费用有限情况下,寿命试验和退化试验分别达到渐近方差最小时的最优试验方案设计,发现退化试验的效率高于寿命试验.研究为小子样条件下长寿命产品可靠性试验设计提供了依据.     

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.     

Weibull小样本失效数据的贝叶斯可靠性分析

随着高性能复杂系统日益向“高质量、长寿命、小子样”等方向发展,在可靠性试验中经常出现小样本失效数据,传统的基于大样本的产品可靠性评估已不能满足试验要求.在两参数Weibull分布模型的基础上,对原始小样本数据采用灰色估计进行两参数估计,再利用蒙特卡洛方法进行随机抽样并将抽样结果纳入贝叶斯先验信息,通过贝叶斯公式得到后验分布,最终计算得到产品可靠度、失效概率、失效率等结果.最后经实例验证了该方法的有效性.     

Modeling of degradation data via wiener stochastic process based on acceleration factor constant principle

This paper proposes a systematic method of modeling accelerated degradation data based on the acceleration factor constant principle. Wiener stochastic process is considered because it is the most extensively used for degradation modeling. For the Wiener stochastic processes with three different time functions, the parameter relationships, which should be satisfied under any two different stress levels, are deduced according to the acceleration factor constant principle. The deduced parameter relationships indicate the stress-related parameters, which are applied to establish accurate accelerated degradation models. In addition, the deduced parameter relationships provide a guidance to test the consistency of the degradation mechanisms under different stress levels. A hypothesis method based on Analysis of Variance is adopted to identify the accelerated stress levels with different degradation mechanism. The degradation data under these stress levels should not be used to assess the product's reliability. The methods of validating accelerated degradation models and reliability assessments are also proposed. The simulation results prove the feasibility and effectiveness of the proposed methods. From the numerical example, it is concluded that the accelerated degradation model established based on the acceleration factor constant principle is more credible and accurate.     

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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.     

Assessing the reliability of a nanocomponent using nanocrack growth

In nanoscience and nanotechnology, much attention has been given to the dual problem of designing nanocomponents with novel physical properties and how such nanocomponents can be fabricated. Receiving less attention has been the question of the nanocomponent's reliability; how does a nanocomponent fail and how long does a nanocomponent survive under typical operating conditions? High reliability is necessary to guarantee the advancement and utilization of nanocomponents due to the fact that they account for a high proportion of costs of newly designed nanosystems as well as multiscale systems. A nanocomponent is a component that is made of atoms, and its reliability is determined by these atoms. There are situations where it is hard or impossible to extract information from a nanocomponent about its relationship to its atoms. In this article, we assess the nanocomponent's reliability by using its physical properties. Specifically, it is known that nanocrack growth involves considerable statistical variability and such variability should be accounted for assessing growth. In this paper, we first provide a stochastic nanocrack growth model and then evaluate the reliability of a nanocomponent based on this model. Various properties of this model are obtained. We also evaluate the reliability of a nanocomponent under different assumptions on our proposed growth model. This paper is a modification of the extensive literature on modeling fatigue cracks in materials on a larger scale, applied to nanoscale where growth is not a function of cumulative stress on the component but related to the time to first exceedance of a threshold. Copyright © 2013 John Wiley & Sons, Ltd.     

铁路应急设施非概率可靠性选址优化研究

为解决小样本、贫信息下铁路应急资源储备点的可靠性选址问题,创新性地将选址-路径问题与区间非概率可靠性方法结合起来,考虑灾情发生后应急设施点在可接受的时间范围内响应受灾点的需求能力及其稳定程度,采用区间值度量路段阻抗,基于区间非概率可靠性理论及区间运算规则,提出路径的非概率可靠性度量及可靠最短路径选择方法;建立基于区间时间阻抗下可靠最短路径的无容量设施选址模型,提出约束条件限制的Monte Carlo改进算法,确定了铁路资源储备点选址的最优方案。实例表明,本文的优化方案能更好地保证救援的时间可靠性,改进的求解算法具有更小的时间复杂度,有效地缩短了运算时间,改善了解的质量。本文的方法与模型体系对于实现铁路应急设施可靠性选址,为决策者提供决策支持,提高铁路应急响应能力具有重要的指导意义。     

Quasi-linear stochastic programming model based on expectation and variance and its application in transportation problem

The stochastic transportation problem involves in many areas such as production scheduling, facility location, resource allocation, logistics management. Constructing an operable solving method has important theoretical and practical value. In this paper, we first analyze the characteristic and deficiencies of the existing stochastic programming methods, such as higher computation complexity. We then give the concept of reliability coefficient and a quasi-linear processing pattern based on expectation and variance. We further analyze the relationship between reliability coefficient and reliability degree, also give the selecting strategy of reliability coefficient. Based on that, we establish a quasi-linear programming model for stochastic transportation problem, and we analyze its performance by a case-based example. The results indicate that this model has good interpretability and operability. It can effectively solve the transportation problem under complex stochastic environment or with incomplete information.     

A Wiener process model with truncated normal distribution for reliability analysis

The use of degradation model to perform the reliability analysis has drawn much attention due to the fact that the performance of numerous highly reliable systems degrades over time. To describe the unit-to-unit variability for a population of systems, the random effect has been incorporated into the degradation model that plays an important part in assessing the reliability of deteriorating systems. In the existing literature, the normal distribution is commonly adopted to represent the random effect, but the assumption can be unsuitable for some practical applications, such as the degradation process of train wheels. In this paper, we present a degradation modeling and reliability estimation approach by using truncated normal distribution to characterize the unit-to-unit variability. A Wiener process with truncated normal distribution is firstly applied to model the degradation process of the deteriorating system, and the analytical expressions of probability density function and reliability function are derived. Expectation maximization algorithm is then used to estimate the model parameters. The effectiveness and feasibility of the presented approach are illustrated through a numerical example and practical case studies for laser devices and train wheels. The results indicate that the presented approach can obtain better reliability estimation results by considering the truncated normal distribution when the unit-to-unit variability has significant difference.     

Optimal solution for novel grey polynomial prediction model

The grey prediction model, as a time-series analysis tool, has been used in various fields only with partly known distribution information. The grey polynomial model is a novel method to solve the problem that the original sequence is in accord with a more general trend rather than the special homogeneous or non-homogeneous trend, but how to select the polynomial order still needs further study. In this paper the tuned background coefficient is introduced into the grey polynomial model and then the algorithmic framework for polynomial order selection, background coefficient search and parameter estimation is proposed. The quantitative relations between the affine transformation of accumulating sequence and the parameter estimates are deduced. The modeling performance proves to be independent of the affine transformation. The numerical example and application are carried out to assess the modeling efficiency in comparison with other conventional models.     

筛选试验情形下可靠性评估

通过改变数据填充方式重新对筛选试验进行可靠性评估.在填充数据的方法上选择等分位点数据填充算法,使得改进后的算法所得的虚拟完全数据更接近于真实的完全数据.最后对所提出的筛选试验情形下可靠性评估方法进行了模拟验证.     

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。     

Mean–variance optimal trading problem subject to stochastic dominance constraints with second order autoregressive price dynamics

The efficient modeling of execution price path of an asset to be traded is an important aspect of the optimal trading problem. In this paper an execution price path based on the second order autoregressive process is proposed. The proposed price path is a generalization of the existing first order autoregressive price path in literature. Using dynamic programming method the analytical closed form solution of unconstrained optimal trading problem under the second order autoregressive process is derived. However in order to incorporate non-negativity constraints in the problem formulation, the optimal static trading problems under second order autoregressive price process are formulated. For a risk neutral investor, the optimal static trading problem of minimizing expected execution cost subject to non-negativity constraints is formulated as a quadratic programming problem. Whereas, for a risk averse investor the variance of execution cost is considered as a measure for the timing risk, and the mean–variance problem is formulated. Moreover, the optimal static trading problem subject to stochastic dominance constraints with mean–variance static trading strategy as the reference strategy is studied. Using Static approximation method the algorithm to solve proposed optimal static trading problems is presented. With numerical illustrations conducted on simulated data and the real market data, the significance of second order autoregressive price path, and the optimal static trading problems is presented.