多态防护系统可靠性及重要度分析

在装备防护系统中,系统中的所有部件都可能会受到攻击,为了降低攻击成功的概率,部件的防护层有多个状态,从完全失效状态到完好状态,部件的防护功能依次增加。本文分析了部件防护层的状态变化规律,研究了当部件受到攻击存活和故障时的平均性能变化机理。然后基于系统结构函数,分别给出了防护层的Birnbaum重要度和综合重要度,分析了当部件防护层的状态发生变化时,系统性能的变化规律和特性;并考虑部件防护层的状态转移率,分析了在多种状态变化情况下的防护层提升对系统性能的影响。最后针对某直升机的航空动力装置系统,分析了不同部件防护层对系统性能的影响,并比较了Birnbaum重要度和综合重要度的不同,验证了提出方法的正确性和有效性。     

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。     

泊松冲击下冷贮备可修系统的可靠性分析

本文研究了一类由有限个同质部件和一个修理工组成的冷贮备可修系统在随机冲击下的可靠性问题。假设冲击以泊松过程到达。当冲击到达时,它会独立地对系统中工作的部件产生影响,而不会对冷贮备部件产生影响。每次冲击的量都服从某一确定的分布,受冲击的部件以一定的概率发生故障,其故障概率是冲击量的函数,当工作的部件发生故障时,下一个冷贮备部件立即开始工作,当所有部件故障时,系统故障,故障部件按故障顺序进行修理,修理时间服从指数分布,故障部件能被修理如新。本文显式给出了系统首次故障前平均时间、稳态可用度、稳态故障频度等可靠性指标。     

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。     

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。     

专职修理工多重休假且修理设备可更换的k/n(G)表决系统研究

讨论专职修理工多重休假,修理设备可发生失效且可更换的k/n(G)表决可修系统.当系统中没有故障部件时,专职修理工开始一次休假,在此期间,若有工作部件发生故障,则立即指派普通修理工修理故障部件,一直持续到系统中无故障部件或专职修理工休假回来.利用马尔可夫过程理论和矩阵解法,给出了系统瞬态和稳态下的可用度和故障频度、可靠度、系统首次故障前的平均时间、修理设备处于更换状态的概率等指标的表达式.在此基础上,基于不同的初始条件研究了相关指标随时间的变化情况.最后,特殊情形的讨论验证了所得结果的正确性.     

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。     

专职修理工多重休假且修理设备可更换的k/n(G)表决系统研究

讨论专职修理工多重休假,修理设备可发生失效且可更换的k/n（G）表决可修系统.当系统中没有故障部件时,专职修理工开始一次休假,在此期间,若有工作部件发生故障,则立即指派普通修理工修理故障部件,一直持续到系统中无故障部件或专职修理工休假回来.利用马尔可夫过程理论和矩阵解法,给出了系统瞬态和稳态下的可用度和故障频度、可靠度、系统首次故障前的平均时间、修理设备处于更换状态的概率等指标的表达式.在此基础上,基于不同的初始条件研究了相关指标随时间的变化情况.最后,特殊情形的讨论验证了所得结果的正确性.     

Poisson冲击下的$k/n(G)$系统的可靠性分析

本文研究了一类Poisson冲击下的$k/n(G)$系统(即$k$-out-of-$n$: $G$系统). 假定冲击的到达数形成一个参数为$\lambda$的Poisson过程, 且冲击的量服从某一分布. 当每次冲击到达时, 对系统中工作的部件独立地产生影响. 进而假定每一部件以一定的概率故障, 概率值是冲击量的函数. 且各次冲击独立地对系统造成损失, 直到工作部件数少于$k$系统故障为止. 在这些假定下, 我们获得了系统的可靠度函数和系统的平均工作时间. 进一步, 假定系统是可修的, 系统中有一个维修工, 并根据``先坏先修’’的维修规则对故障部件进行维修. 在维修时间服从指数分布的假设下, 系统状态转移服从Markov过程. 对该系统我们建立了状态转移方程, 并求得了系统可用度、稳态下的平均工作时间、平均停工时间和系统失效频率等可靠性指标. 最后, 我们还给出了一个简单例子来演示讨论的模型.     

基于GSPN的实施延迟策略生产系统建模与性能分析

延迟策略是协调市场需求变化的绝对性和生产系统相对稳定性矛盾的一个重要战略手段。为分析实施延迟策略生产系统的性能,利用随机广义Petri-net(GSPN)的建模分析方法,并根据GSPN与马尔科夫链的同构关系,将GSPN模型转化成为等价的马尔科夫链模型。通过马尔科夫链及相关数学方法获得实施延迟策略生产系统的主要性能指标。该方法不仅可以分析实施延迟策略生产系统的整体性能,而且能够对系统的各个环节运作效率做出定量分析。最后,通过算例分析验证了该方法的科学性与有效性,丰富了延迟策略研究方面的方法理论。     

Optimal maintenance policy in a multistate deteriorating standby system

An optimal maintenance policy for a multistate deteriorating standby system is proposed in this study. Traditionally, a system could only presume two operational states: success or failure, and the maintenance policy is to determine the optimal number of standby components, subject to factors such as maintenance capability, cost of the standby items, etc., so as to minimize the operational cost. This study considers a more general production system in which progressive deterioration is incurred during the operating time, hence resulting in degrading performance. By modeling the system as a multistate deteriorating system, an optimal maintenance policy is obtained by determining the optimal number of standby components required in the system and the optimal state in which the replacement of deteriorating components shall be made.     

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.     

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.     

具有修理延迟的两不同部件冷贮备系统的可靠性分析

研究了有修理延迟的两个不同部件和两个修理工组成的冷贮备系统.假定部件的工作寿命服从一般分布,故障后的延迟修理时间和修理时间均服从指数分布.利用马尔可夫更新过程、拉普拉斯变换和拉普拉斯-司梯阶变换工具,得到了系统的首次故障前时间、可用度和平均故障次数等可靠性指标.     

Multi-state Systems with Graduate Failure and Equal Transition Intensities

We consider unrecoverable homogeneous multi-state systems with graduate failures, where each component can work at M + 1 linearly ordered levels of performance. The underlying process of failure for each component is a homogeneous Markov process such that the level of performance of one component can change only for one level lower than the observed one, and the failures are independent for different components. We derive the probability distribution of the random vector X, representing the state of the system at the moment of failure and use it for testing the hypothesis of equal transition intensities. Under the assumption that these intensities are equal, we derive the method of moments estimators for probabilities of failure in a given state vector and the intensity of failure. At the end we calculate the reliability function for such systems. Received: May 18, 2007., Revised: July 8, 2008., Accepted: September 29, 2008.     

Probabilistic Modelling of Monitoring and Maintenance of Multistate Monotone Systems with Dependent Components

In Gåsemyr and Natvig (2001) partial monitoring of components with applications to preventive system maintenance was considered for a binary monotone system of binary components. The purpose of the present paper is to extend this to a multistate monotone system of multistate components, where the states more realistically represent successive levels of performance ranging from the perfect functioning level down to the complete failure level. We start out close to the spirit of Arjas (1989) by using a marked point process with complete monitoring of all components, and hence of the system, as the basic reference framework. We then consider a marked point process linked to partial monitoring of some components, for instance in certain time intervals. Incorporation of information from the observed system history process is then treated. Mainly, we assume that the inspection strategy is determined by the observed component history process only, with a possible exception of a full or partial autopsy after an observed change of state of , the system. Furthermore, we consider how to arrive at the posterior distribution for the relevant parameter vector by a standard simulation procedure, the data augmentation method. The idea is to extend the observed data to the complete component history process. The theory is applied to an electrical power generation system for two nearby oilrigs with some standby components, as considered in Natvig et al. (1986).AMS 2000 Subject Classification: Primary 96B25; Secondary, 62N05, 60K10