基于马尔科夫过程的储备系统稳态性能分析

由于储备系统组成部件在存储期间的失效概率各不相同,当部件状态趋于稳定时,各个状态对系统性能的影响也存在差异。为了识别关键部件及其状态对系统性能的影响程度,本文以重要度为主要指标,应用马尔科夫过程研究储备系统在稳态时的性能变化模式。首先基于综合重要度研究系统性能的变化规律,并结合冷储备系统和温储备系统的状态转移矩阵推导出马尔科夫过程中稳态值的计算方法;其次基于稳态综合重要度获得系统稳态时的性能变化模式;最后以双臂机器人为例,分析部件处于不同状态时对系统性能的影响模式,比较了不同部件综合重要度的变化,验证了提出方法的有效性。     

多态系统部件维修成本评估分析

多态系统是可靠性理论中的一种重要系统,是指部件和系统具有多个状态。随着部件的劣化,系统的性能随之下降。为了提高系统的性能,部件需要进行一定的维修。本文考虑部件状态转移率与停留时间的关系,利用重要度理论来分析部件维修成本的变化规律,得出对系统维修成本影响最大的部件。首先,基于维修成本的函数关系,给出了重要度的表达式;其次,针对典型串联系统和并联系统,随着时间推移,给出维修成本的变化规律;最后算例仿真验证了提出方法的有效性和正确性。     

多态关联系统结构重要度分析

多态关联系统重要度是可靠性分析的重要研究内容之一，它可用于可靠度分配，系统的优化设计和指导系统运行管理等。本文定义了５类物理意义明确的结构重要度，由实例看出，这些重要度能较好反映系统状态的性质及部件对系统状态的影响。     

基于系统可靠性的组件综合重要度变化机理分析

重要度理论是一种重要的系统薄弱环节识别和评估方法,被广泛应用于系统可靠性设计优化、维修资源分配、维修决策以及风险分析等领域。本文以组件状态转移率为纽带,分析了组件综合重要度对系统可靠性的影响机理,识别对系统可靠性变化影响最大的组件,综合重要度评估了单位时间内系统可靠性的变化。首先给出综合重要度的定义;其次讨论系统可靠性的组件重要度表示方法;最后在串联和并联系统中,分析综合重要度随着组件状态转移率的变化机理。     

考虑负载共享的线性连续k-out-of-n:F系统检测策略优化

针对线性连续k-out-of-n:F系统提出定期检测策略,每隔固定周期对系统中各部件状态进行检查并以新部件更换故障部件,若系统发生故障则立即停机并更换故障部件.由于受到负载共享的影响,各工作部件故障率不仅与系统中发生故障的部件数量相关,还与其相邻部件状态有关;为此,引入损伤故障率模型描述部件故障率.基于故障序列图和更新报酬理论建立定期检测策略优化模型以最小化单位时间内的期望成本,确定最优检测周期.最后,通过算例分析验证模型的有效性.     

基于综合重要度的动车组多部件系统机会维修策略研究

针对我国动车组列车现行维修方式,提出基于综合重要度序列的动车组多部件系统机会维修策略,对提高系统可靠度贡献大的关键部件进行准时优先维修。建立部件综合重要度指数计算模型,并依据其对部件维修优先级进行排序。以维修总成本最低为目标计算单部件最优维修周期及时刻,以系统维修总成本最低为目标,以关键部件的维修时刻为系统停机时刻建立考虑重要度的多部件系统机会维修模型。算例选取某型动车组四级修时更换的四部件系统为研究对象,讨论机会维修里程窗的大小及其偏移量对维修效果的影响,对比结果表明,考虑综合重要度的机会维修策略能够在维修费用基本持平的条件下,保证对系统可靠性贡献大的关键部件的可靠性,进而保证系统的整体可靠性。     

修复率可变化的表决系统可靠性分析

研究了具有维修速率可变化的k/n(G)表决可修系统,其中部件的工作时间和修理时间均服从负指数分布.开始时,当系统中的故障部件数小于某一阈值L时,修理工以较低的维修率修理故障的部件.如果修理工修理工作进展不顺利,故障部件数增加到阈值L时,将立即以较快的速度修理故障部件,此状态一直持续到系统中没有故障部件为止.使用马尔可夫过程理论和分析方法,得到了系统可用度、故障频度、系统首次故障前的平均时间等指标的表达式.进一步,讨论了不同条件下系统相关指标随系统参数变化的情况,并通过对特殊情形的讨论数值验证了所得结果的正确性.     

多约束条件下三状态复杂系统的优化分配

研究了三状态复杂系统在多约束条件下可靠度的问题.以串-并联系统为研究对象,利用选取重要度来提高系统可靠度的方法,获得了重要度对系统可靠度有着重要影响的结果,并通过算法、例题对实例进行了验证.     

复杂可修复系统故障数据分析处理中的两个问题

马良河等．复杂可修复系统故障数据分析处理中的两个问题．数理统计与管理，１９９８，１７（３），３７～４０．本文给出了利用部件的故障数据对复杂可修复系统的可靠性状态进行分析和估计时应首先解决的两个问题：应选取合适的故障数据及应分析系统部件的固有属性对故障数据的影响。进而给出了这两个问题的解决方法及对系统性能的分析方法。最后针对飞机发动机的一组故障数据给出了计算实例。所用工具主要是假设检验和方差分析的理论     

基于Copula的部件相依并联系统可靠性分析

应用Copula函数,研究了部件相依的并联系统可靠性问题,给出了F-G-M Copula函数下并联系统的可靠度、平均寿命、失效率的表达式,讨论了部件下象限相关下并联系统的平均寿命与部件独立时系统的平均寿命的关系,通过算例分析了部件个数与部件相依关系对系统平均寿命的影响。     

Strategic defense and attack for series and parallel reliability systems

A system of independent components is defended by a strategic defender and attacked by a strategic attacker. The reliability of each component depends on how strongly it is defended and attacked, and on the intensity of the contest. In a series system, the attacker benefits from a substitution effect since attacker benefits flow from attacking any of the components, while the defender needs to defend all components. Even for a series system, when the attacker is sufficiently disadvantaged with high attack inefficiencies, and the intensity of the contest is sufficiently high, the defender earns maximum utility and the attacker earns zero utility. The results for the defender (attacker) in a parallel system are equivalent to the results for the attacker (defender) in a series system. Hence, the defender benefits from the substitution effect in parallel systems. With budget constraints the ratio of the investments for each component, and the contest success function for each component, are the same as without budget constraints when replacing the system values for the defender and attacker with their respective budget constraints.     

Component importance based on dependence measures

We discuss the construction of component importance measures for binary coherent reliability systems from known stochastic dependence measures by measuring the dependence between system and component failures. We treat both the time-dependent case in which the system and its components are described by binary random variables at a fixed instant as well as the continuous time case where the system and component life times are random variables. As dependence measures we discuss covariance and mutual information, the latter being based on Shannon entropy. We prove some basic properties of the resulting importance measures and obtain results on importance ordering of components.     

基于成本优先级的部件维修策略分析

现实中,系统由于任务、环境等因素,无法实时对故障部件进行维修。因此需要在任务间隔期间或对故障部件进行维修的同时对系统各部件进行预防性机会维修。本文考虑系统期望维修成本,提出了基于部件维修优先级的预防性维修策略。首先把系统期望维修成本分为失效部件维修成本、失效部件导致系统故障的成本和预防性维修其他部件的成本,提出了基于成本的二态和多态系统部件维修优先级度量方法,并在两种场景下分析了如何选择预防性维修部件。其次针对多态系统,研究了基于成本重要度的部件最佳维修水平,并讨论了成本约束下的部件预防性维修策略。最后以某型预警机系统为例进行验证,结果表明,基于成本的预防性维修策略不仅与故障部件位置和相关成本有关,而且还与可用于预防性维修的其他部件重要性有关。     

Measures of Component Importance in Nonrepairable and Repairable Multistate Strongly Coherent Systems

In Natvig and Gåsemyr (Methodol Comput Appl Probab 11:603–620, 2009) dynamic and stationary measures of importance of a component in a binary system were considered. To arrive at explicit results the performance processes of the components were assumed to be independent and the system to be coherent. Especially the Barlow–Proschan and the Natvig measures were treated in detail and a series of new results and approaches were given. For the case of components not undergoing repair it was shown that both measures are sensible. Reasonable measures of component importance for repairable systems represent a challenge. A basic idea here is also to take a so-called dual term into account. For a binary coherent system, according to the extended Barlow–Proschan measure a component is important if there are high probabilities both that its failure is the cause of system failure and that its repair is the cause of system repair. Even with this extension results for the stationary Barlow–Proschan measure are not satisfactory. For a binary coherent system, according to the extended Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. With this extension the results for the stationary Natvig measure seem very sensible. In the present paper most of these results are generalized to multistate strongly coherent systems. For such systems little has been published until now on measures of component importance even in the nonrepairable case.     

Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

This paper presents an integrated platform for multi-sensor equipment diagnosis and prognosis. This integrated framework is based on hidden semi-Markov model (HSMM). Unlike a state in a standard hidden Markov model (HMM), a state in an HSMM generates a segment of observations, as opposed to a single observation in the HMM. Therefore, HSMM structure has a temporal component compared to HMM. In this framework, states of HSMMs are used to represent the health status of a component. The duration of a health state is modeled by an explicit Gaussian probability function. The model parameters (i.e., initial state distribution, state transition probability matrix, observation probability matrix, and health-state duration probability distribution) are estimated through a modified forward–backward training algorithm. The re-estimation formulae for model parameters are derived. The trained HSMMs can be used to diagnose the health status of a component. Through parameter estimation of the health-state duration probability distribution and the proposed backward recursive equations, one can predict the useful remaining life of the component. To determine the “value” of each sensor information, discriminant function analysis is employed to adjust the weight or importance assigned to a sensor. Therefore, sensor fusion becomes possible in this HSMM based framework.     

New Results on the Barlow–Proschan and Natvig Measures of Component Importance in Nonrepairable and Repairable Systems

In this paper dynamic and stationary measures of importance of a component in a binary system are considered. To arrive at explicit results we assume the performance processes of the components to be independent and the system to be coherent. Especially, the Barlow–Proschan and the Natvig measures are treated in detail and a series of new results and approaches are given. For the case of components not undergoing repair it is shown that both measures are sensible. Reasonable measures of component importance for repairable systems represent a challenge. A basic idea here is also to take a so-called dual term into account. According to the extended Barlow–Proschan measure a component is important if there are high probabilities both that its failure is the cause of system failure and that its repair is the cause of system repair. Even with this extension results for the stationary Barlow–Proschan measure are not satisfactory. According to the extended Natvig measure a component is important if both by failing it strongly reduces the expected system uptime and by being repaired it strongly reduces the expected system downtime. With this extension the results for the stationary Natvig measure seem very sensible.     

A novel optimal preventive maintenance policy for a cold standby system based on semi-Markov theory

A novel optimal preventive maintenance policy for a cold standby system consisting of two components and a repairman is described herein. The repairman is to be responsible for repairing either failed component and maintaining the working components under certain guidelines. To model the operational process of the system, some reasonable assumptions are made and all times involved in the assumptions are considered to be arbitrary and independent. Under these assumptions, all system states and transition probabilities between them are analyzed based on a semi-Markov theory and a regenerative point technique. Markov renewal equations are constructed with the convolution of the cumulative distribution function of system time in each state and corresponding transition probability. By using the Laplace transform to solve these equations, the mean time from the initial state to system failure is derived. The optimal preventive maintenance policy that will provide the optimal preventive maintenance cycle is identified by maximizing the mean time from the initial state to system failure, and is determined in the form of a theorem. Finally, a numerical example and simulation experiments are shown which validated the effectiveness of the policy.