受延迟随机损伤系统可修复时间的分布特征与修复概率

研究受延迟随机损伤系统可修复时间的概率特性．即系统初始运行安全期时间长度为一随机变量,受Poisson过程规律的随机冲击并产生随机损伤．用条件随机过程和条件Markov过程为数学工具,求出系统可修复时间的密度函数与特征函数以及可修复概率．     

Factorization and Stochastic Decomposition Properties in Bulk Queues with Generalized Vacations

This paper considers a class of stationary batch-arrival, bulk-service queues with generalized vacations. The system consists of a single server and a waiting room of infinite capacity. Arrivals of customers follow a batch Markovian arrival process. The server is unavailable for occasional intervals of time called vacations, and when it is available, customers are served in groups of fixed size B. For this class of queues, we show that the vector probability generating function of the stationary queue length distribution is factored into two terms, one of which is the vector probability generating function of the conditional queue length distribution given that the server is on vacation. The special case of batch Poisson arrivals is carefully examined, and a new stochastic decomposition formula is derived for the stationary queue length distribution.AMS subject classification: 60K25, 90B22, 60K37     

An extended stochastic failure model for a system subject to random shocks

In this article, a stochastic failure model for a system subject to a random shock process is studied. It is assumed that a fatal shock results in an immediate system failure, whereas a non-fatal shock may increase the susceptibility of the system to failure. The lifetime distribution of the system and its failure rate function are derived, and the effect of environmental factors on the failure process of the system is also investigated. Lifetimes of systems operated under different environmental conditions are stochastically compared.     

Natural gradient algorithm for stochastic distribution systems with output feedback

In this paper, we use natural gradient algorithm to control the shape of the conditional output probability density function for the stochastic distribution systems from the viewpoint of information geometry. The considered system here is of multi-input and single output with an output feedback and a stochastic noise. Based on the assumption that the probability density function of the stochastic noise is known, we obtain the conditional output probability density function whose shape is only determined by the control input vector under the condition that the output feedback is known at any sample time. The set of all the conditional output probability density functions forms a statistical manifold (M), and the control input vector and the output feedback are considered as the coordinate system. The Kullback divergence acts as the distance between the conditional output probability density function and the target probability density function. Thus, an iterative formula for the control input vector is proposed in the sense of information geometry. Meanwhile, we consider the convergence of the presented algorithm. At last, an illustrative example is utilized to demonstrate the effectiveness of the algorithm.     

State estimation for partially observed Markov chains

Recursive equations are derived for the conditional distribution of the state of a Markov chain, given observations of a function of the state. Mainly continuous time chains are considered. The equations for the conditional distribution are given in matrix form and in differential equation form. The conditional distribution itself forms a Markov process. Special cases considered are doubly stochastic Poisson processes with a Markovian intensity, Markov chains with a random time, and Markovian approximations of semi-Markov processes. Further the results are used to compute the Radon-Nikodym derivative for two probability measures for a Markov chain, when a function of the state is observed.     

Stochastic stabilization of first-passage failure of Rayleigh oscillator under Gaussian White-Noise parametric excitations

Stochastic stabilization of first-passage failure of Rayleigh oscillator under Gaussian White-Noise parametric excitation is studied. The equation of motion of the system is first reduced to an averaged Itô stochastic differential equation by using the stochastic averaging method. Then, a backward Kolmogorov equation governing the conditional reliability function of first-passage failure is established. The conditional reliability function, and the conditional probability density are obtained by solving the backward Kolmogorov equation with boundary conditions. Finally, the cost function and optimal control forces are determined by the requirements of stabilizing the system by evaluating the maximal Lyapunov exponent. The numerical results show that the procedure is effective and efficiency.     

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.     

First-passage failure of linear oscillator with non-classical inelastic impact

The first-passage failure of linear oscillator with inelastic impact subjected to the additive and multiplicative random noises is investigated. The impact is described by the non-classical inelastic impact model, which is essentially different from the traditional impact model and can provide the whole information of the impact process. First of all, the impact force in the motion equation is replaced by the quasi-linear damping and nonlinear stiffness terms. Then, the stochastic averaging is adopted and the averaged Itô stochastic deferential equation of the total system energy is derived. Last, by solving the established backward Kolmogorov equation and Pontryagin equation from the averaged Itô equation numerically, the conditional reliability, the conditional probability density function (PDF) and the mean time of first-passage failure can be obtained. The comparison between the analytical results and those from Monte-Carlo simulation reveals the proposed procedure is effective. The influences of some system parameters are discussed in detail.     

Symbolic and numeric scheme for solution of linear integro-differential equations with random parameter uncertainties and Gaussian stochastic process input

The paper describes a theoretical apparatus and an algorithmic part of application of the Green matrix-valued functions for time-domain analysis of systems of linear stochastic integro-differential equations. It is suggested that these systems are subjected to Gaussian nonstationary stochastic noises in the presence of model parameter uncertainties that are described in the framework of the probability theory. If the uncertain model parameter is fixed to a given value, then a time-history of the system will be fully represented by a second-order Gaussian vector stochastic process whose properties are completely defined by its conditional vector-valued mean function and matrix-valued covariance function. The scheme that is proposed is constituted of a combination of two subschemes. The first one explicitly defines closed relations for symbolic and numeric computations of the conditional mean and covariance functions, and the second one calculates unconditional characteristics by the Monte Carlo method. A full scheme realized on the base of Wolfram Mathematica and Intel Fortran software programs, is demonstrated by an example devoted to an estimation of a nonstationary stochastic response of a mechanical system with a thermoviscoelastic component. Results obtained by using the proposed scheme are compared with a reference solution constructed by using a direct Monte Carlo simulation.     

Block-Structured Fluid Queues Driven by QBD Processes

Abstract

In this paper, a unified approach for studying block-structured fluid models is proposed by means of the RG-factorization. When the stochastic environment (or background) is assumed to be a quasi-birth-and death (QBD) process, with either infinitely many levels or finitely many levels, the Laplace transform for the stationary probability distribution of the buffer content is expressed in terms of the R-measure. At the same time, the Laplace-Stieltjes transforms for both the conditional distribution and the conditional mean of a first passage time in such a fluid queue are derived by the same approach.     

Remarks on the optimization process of a manufacturing system with stochastic breakdown and rework

This paper reexamines the optimization process of a manufacturing system with stochastic breakdown and rework proposed by Chiu [S.W. Chiu, An optimization problem of manufacturing systems with stochastic machine breakdown and rework process, Applied Stochastic Models in Business and Industry 24 (2008) 203–219]. The proof of convexity of the long-run average cost function for the aforementioned manufacturing system is provided in this note. It can be used to replace the conditional convexity given in Theorem 1 of Chiu (2008) [1]. Therefore, when determining the optimal solution for such a real-life system, computational efforts in verifying the conditional convexity can now be omitted, due to the improved quality of the optimization process.     

Stochastic analysis of life insurance surplus

The aim of the paper is to examine the behavior of insurance surplus over time for a portfolio of homogeneous life policies. We distinguish between stochastic and accounting surpluses and derive their first two moments. A recursive formula is proposed for calculating the distribution function of the accounting surplus. We then examine the probability that the accounting surplus becomes negative in a given insurance year. Numerical examples illustrate the results for portfolios of temporary and endowment life policies assuming a conditional AR(1) process for the rates of return.     

On a Stochastic Survival Model for a System Under Randomly Variable Environment

In many cases, the survival probability of a system depends not only on the intrinsic characteristic of the system itself but also on the randomly variable external environment under which the system is being operated. In this paper we study a stochastic survival model for a system under random shock process which affects the survival of the system in a complicated way. The lifetime distribution of the system is derived, and the effect of environmental factors on the failure process of the system is also investigated.     

A mathematical model for discrete-time buffer systems with correlated output process

This article considers an infinite buffer system with one or more input channels and multiple output channels. Transmission of messages from the buffer is synchronous and the arrival process of messages to the buffer is general. Each of the output channels is subjected to a random interruption process, i.e., the number of available output channels varies in time and is stochastic. The analysis of this system is carried out under the assumption that the output process can be described as a first order Markov process, i.e., the probability distribution of the number of available output channels during some clock time interval depends only on the number of available output channels during the previous clock time interval.A set of equations describing the behavior of this buffer system is derived. For a number of interesting special cases this set is solved and explicit expressions are obtained for the probability generating function of the number of messages in the buffer. Several prior studies are found as special cases of the present one. An illustrative example is treated and the results are compared to the ones obtained for an uncorrelated output process with the same equilibrium distribution. Some considerable deviations from these results are found.     

多服务台排队系统的组装策略

本文讨论了(e,d)-策略多重休假的M/M/c排队,这是—个M/M/c-e和M/M/c排队的“组装”策略,为系统设计提供更大的灵活性．使用拟生灭过程方法,给出了稳态队长分布．进一步地,利用条件Erlang分布的一些有趣的性质,给出了等待时间分布的简洁表达式．最后,得到稳态指标的条件随机分解结果．     

启动延迟N-策略批到达多重休假排队

目前N-策略批到达排队系统稳态队长分布难以给出解析解.提出一种新的递归算法研究顾客批到达,服务台延迟启动且多重休假的N-策略休假排队系统稳态队长分布.首先采用条件随机分解的方法得到稳态队长分布的概率母函数;然后采用递归算法推导附加队长分布的解析表达式;最后推导出稳态队长分布的递推关系式.     

An alternative approach to nonlinear filtering

The structure of a nonlinear filter with observation process having continuous and discontinuous components is considered. The approach is based on the so-called “Bayes” formula for conditional expectations. “Fubini” type theorems for stochastic integrals are given and used to obtain the representations of an optimal estimate and of the conditional likelihood ratio. A linear unnormalized filtering equation for controlled system process is derived.     

Mixture representation for the residual lifetime of a repairable system

In this paper, we consider a repairable system in which two types of failures can occur on each failure. One is a minor failure that can be corrected with minimal repair, whereas the other type is a catastrophic failure that destroys the system. The total number of failures until the catastrophic failure is a positive random variable with a given probability vector. It is assumed that there is some partial information about the failure status of the system, and then various properties of the conditional probability of the system failure are studied. Mixture representations of the reliability function for the system in terms of the reliability function of the residual lifetimes of record values are obtained. Some stochastic properties of the conditional probabilities and the residual lifetimes of two systems are finally discussed. Copyright © 2017 John Wiley & Sons, Ltd.     

On the characteristic functional of a doubly stochastic Poisson process: Application to a narrow-band process

The characteristic functional (c.fl.) of a doubly stochastic Poisson process (DSPP) is studied and it provides us the finite dimensional distributions of the process and so its moments. It is also studied the case of a DSPP which intensity is a narrow-band process. The Karhunen–Loève expansion of its intensity is used to obtain the probability distribution function and a decomposition of this Poisson process. The covariance derived from the general c.fl. is applied in this particular DSPP.     

全概率公式的推广及其应用

全概率公式及其思想在概率统计与随机过程中具有重要作用.给出了公式在条件概率下的推广形式与具体应用.同时,得到了独立性条件下的特殊形式.