NHPP软件可靠性模型参数的保序估计

许多主要的非齐次泊过程松模型的故障强度都满足顺序约束条件.在顺序约束条件下,本文提出了故障强度的约束极大似然估计(RML),并讨论了其性质.利用故障强度的RML,获得了软件可靠性模型参数的加权最小二乘估计.     

常利率下Cox风险过程的罚金折现期望函数

本文考虑了常利率环境下Cox风险模型的罚金折现期望值，利用后向差分法，得到了条件期望值与平稳情形时的期望值分别所满足的积分方程．并且，给出了一个强度过程为二状态马尔可夫过程及索赔服从指数分布的例子．     

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.     

有两种故障状态的两部件串联系统的预防维修策略

本文研究的是由两个部件串联组成且有两种故障状态的系统的预防维修策略, 当系统的工作时间达到T时进行预防维修, 预防维修使部件恢复到上一次故障维修后的状态。每个部件发生故障都有两种状态, 可维修和不可维修。当部件的故障为可维修故障时, 修理工对其进行故障维修, 且每次故障维修后的工作时间形成随机递减的几何过程, 每次故障后的维修时间形成随机递增的几何过程。当部件发生N次可维修故障或一次不可维修故障时进行更换。以部件进行预防维修的间隔和更换前的可维修故障次数N组成的二维策略(T, N) 为策略, 利用更新过程和几何过程理论求出了系统经长期运行单位时间内期望费用的表达式, 并给出了具体例子和数值分析。     

基于特征参数分布的机载电子设备故障诊断与预测

基于特征参数趋势进化的故障预测是一种有效的方法,引入了一种考虑特征参数概率分布的新型判据进行多故障模式诊断与预测.基于过程神经网络建立了高精度预测模型,根据模型和部件使用记录进行趋势预测.基于方法对机载电子设备进行案例研究,结果表明,方法的判定结果更加符合多故障模式并存、故障严重程度不同的实际情况,而具有较高拟和、泛化预测精度的PNN模型是一种有效的趋势预测方法.     

具有两阶段故障的流模型近似解分析

本文分析了具有两阶段故障策略的流模型近似解.系统首次遭遇故障时将进入部分故障期,期间缓冲器以较低服务速率继续提供服务.若在部分故障期内缓冲器再次遭遇故障,则完全停止工作.对于具有此策略的流模型,借助归一化技术(Uniformization Technique),通过递推得到了该流模型库存量尾分布的近似解,并给出了库存量的各阶矩.最后将该流模型应用于云计算服务中由硬件故障而导致的排队分析中,利用数值例子分析系统参数对整体系统性能指标的影响.     

工作时间受限的单部件可修系统的维修策略

基于几何过程理论,研究了一类工作时间受限的单部件可修系统的最优更换策略问题.假定系统的维修时间和工作时间都服从一般分布,当工作时间低于预先给定的阈值φ,或当系统的维修次数达到N时,不再维修,而是更换上全新系统.利用更新过程理论,得到了系统平均故障频度和平均可用度等可靠性指标,并给出了系统长期运行单位时间期望效益函数的表达式,最后通过数值模拟讨论了下限阈值和工作次数对最优策略的影响.     

扩散过程依全变差范数的收敛速度

本文使用耦合方法,通过对耦合时间的矩的估计得到紧流形上扩散过程依全变差范数指数式收敛的结果;并利用非零第一特征值与特征函数,给出了另外两个估计.     

基于空间故障树理论的系统故障定位方法研究

为了系统发生不同类型故障后快速定位可能引起该故障的系统元件,通过分析系统结构和元件故障概率分布,以及系统在不同工作环境中发生各类型故障的统计数量,提出基于空间故障树(Space Fault Tree,SFT)理论的系统故障定位方法.该方法使用SFT概念得到系统内部结构及元件的故障概率矩阵P(X_i),分析元件X_i故障对于所在割集S_j及系统T故障的贡献度,结合系统故障次数统计矩阵Γ(m_q),最终得到元件X_(1~I)与故障m_(1~Q)的相关度矩阵.这个矩阵可反映出对于任意系统故障m_q与故障元件X_(1~I)的相关性排序、对应的割集、及保证结论正确的可能性,还可优化系统故障分类.实例研究表明:方法可确定各故障的至因故障元件,并根据可能性进行排序,排序靠前的元件组合正是系统的割集,这从侧面也说明了方法正确性.     

关于机器随机故障完工时间方差最小化单机调度问题

讨论了机器随机故障时,工件完工时间方差的期望最小化单机调度问题,其中描述机器故障的计数过程为广义泊松过程.推导出了目标函数等价的确定形式,而后进一步给出了工件加工时间相同时问题的最优解.     

多台系统可靠性增长试验方法研究

提出了一种解决多台系统同步投试、同步停止试验和同步改进问题的新模型——指数模型．该模型充分考虑了增长过程中的各种可得信息,包括各改进阶段的失效数、未失效数和失效时间等数据．如果多台系统经过多次同步改进,并且单台系统的可靠性增长符合AMSAA模型,就可以合理地认为在每两个相邻的改进时刻之间,每台系统的失效时间服从指数分布．采用非参数方法得到多台系统在各同步停止试验时刻的可靠度,并利用最小二乘法拟合求得该模型中参数a和b的点估计值,以及参数b的置信限．通过在工程实例中对所提模型和几种已有模型计算结果的比较,说明了所提模型在解决多台系统同步可靠性增长问题中的合理性．     

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.     

基于Markov更新过程的城市交通网络级联失效评价模型

根据路段单元状态与其功能之间的关系,给出了路段单元状态的‘失效—非失效’二态表示方法,进一步根据网络中路段单元之间的连接关系,提出了道路交通网络‘级联失效’态的定义及识别方法;利用更新理论及Markov链相关理论,分析了道路交通网络级联失效态—非级联失效态持续时间随机变化的概率分布规律,给出了对假想分布的未知参数进行估计及对假想分布进行假设检验的方法,并提出了以失效次数及转移概率为主要评价参数的交通网络级联失效评价模型。以一个实际路网为例,对模型进行了标定,将标定好的模型评价结果与实际观察结果进行了比对,结果显示模型具有较好的实用性。     

Semi-Markov failure rates processes

Usually, a reliability function is defined by a failure rate which is a real function taking the non-negative real values. In this paper the failure rate is assumed to be a stochastic process with non-negative and right continuous trajectories. The reliability function is defined as an expectation of a function of that random process. Particularly, the failure rate defined by the semi-Markov processes is considered here. The theorems dealing with the renewal equations for the conditional reliability functions with a semi-Markov process as a failure rate are presented in this paper. A system of that kind of equations for the discrete state space semi-Markov process is applied for calculating the reliability function for the 3-states semi-Markov random walk. Using the introduced system of renewal equations for the countable state space, the reliability function for the Furry-Yule failure rate process is obtained.     

海冰弯曲破坏下的破坏准则数值模拟方法研究

海冰的弯曲破坏进程的研究已经称为冰工程界的一项重要课题.通过理论分析结合数值计算对海冰弯曲破坏数值模拟方法进行探索:探讨适合于海冰弯曲应力状态下的本构模型和破坏准则;在LS-DYNA中模拟圆环形冰排弯曲破坏和冰排与锥体结构相互作用的进程,提出了一种对海冰破坏准则验证的技术思路;通过所提出的技术思路对基于三轴压缩试验的Derradji破坏准则进行了修正,使其能够适用于海冰的弯曲破坏.     

Bayesian process optimization using failure amplification method

This work is motivated by a problem of optimizing printed circuit board manufacturing using design of experiments. The data are binary, which poses challenges in model fitting and optimization. We use the idea of failure amplification method to increase the information supplied by the data and then use a Bayesian approach for model fitting. The Bayesian approach is implemented using Gaussian process models on a latent variable representation. It is demonstrated that the failure amplification method coupled with a Bayesian approach is highly suitable for optimizing a process with binary data. Copyright © 2010 John Wiley & Sons, Ltd.     

A simulation-based multivariate Bayesian control chart for real time condition-based maintenance of complex systems

When complex systems are monitored, multi-observations from several sensors or sources may be available. These observations can be fused through Bayesian theory to give a posterior probabilistic estimate of the underlying state which is often not directly observable. This forms the basis of a Bayesian control chart where the estimated posterior probability of the state can be compared with a preset threshold level to assess whether a full inspection is needed or not. Maintenance can then be carried out if indicated as necessary by the inspection. This paper considers the design of such multivariate Bayesian control chart where both the transition between states and the relationship between observed information and the state are not Markovian. Since analytical or numerical solutions are difficult for the case considered in this paper, Monte Carlo simulation is used to obtain the optimal control chart parameters, which are the monitoring interval and the upper control limit. A two-stage failure process characterised by the delay time concept is used to describe the underlying state transition process and Bayesian theory is used to compute the posterior probability of the underlying state, which is embedded in the simulation algorithm. Extensive examples are shown to demonstrate the modelling idea.     

A novel cognitive transformation algorithm based on Gaussian cloud model and its application in image segmentation

The representation and processing of uncertain concepts are key issue for both the study of artificial intelligence with uncertainty and human knowledge processing. The intension and extension of a concept can be transformed automatically in the human cognition process, while it is difficult for computers. A Gaussian cloud model (GCM) is used to realize the cognitive transformation between intension and extension of a concept through computer algorithms, including forward Gaussian cloud transformation (FGCT) algorithms and backward Gaussian cloud transformation (BGCT) algorithms. A FGCT algorithm can transform a concept’s intension into extension, and a BGCT algorithm can implement the cognitive transformation from a concept’s extension to intension. In this paper, the authors perform a thorough analysis on the existing BGCT algorithms firstly, and find that these BGCT algorithms have some drawbacks. They cannot obtain the stable intension of a concept sometimes. For this reason, a new backward Gaussian cloud cognitive transformation algorithm based on sample division is proposed. The effectiveness and convergence of the proposed method is analyzed in detail, and some comparison experiments on obtaining the concept’s intension and applications to image segmentation are conducted to evaluate this method. The results show the stability and performance of our method.     

非Gauss随机特性下的结构首次失效时间研究

提出了一个基于结构响应矩的解析方法, 用来计算具有非Gauss特性结构的首次失效时间．在该方法中,首先采用其系数可通过结构反应矩（偏态系数和峰度系数等）计算的幂级数,将非Gauss结构反应变换为标准Gauss过程．然后,利用变换的标准Gauss过程计算原结构反应过程关于某临界界限的平均超越率、平均群超尺度和初始超越概率．最后,在修正超越率为独立的假定下,建立了首次超越时间的计算公式．Gauss过程激励下非线性单自由度振动系统的分析,不仅说明了该方法的应用过程,也通过与Monte Carlo模拟和传统Gauss模型方法的对比分析,证明了该方法的精确性和效率．