Reliability Analysis of Phased-Mission System With Independent Component Repairs

This paper proposes a hierarchical modeling approach for the reliability analysis of phased-mission systems with repairable components. The components at the lower level are described by continuous time Markov chains which allow complex component failure/repair behaviors to be modeled. At the upper level, there is a combinatorial model whose structure function is represented by a binary decision diagram (BDD). Two BDD ordering strategies, and consequently two evaluation algorithms, are proposed to compute the phased-mission system (PMS) reliability based on Markov models for components, and a BDD representation of system structure function. The performance of the two evaluation algorithms is compared. One algorithm generates a smaller BDD, while the other has shorter execution time. Several examples, and experiments are presented in the paper to illustrate the application, and the advantages of our approach.     

Reliability Modeling Using SHARPE

Combinatorial models such as fault trees and reliability block diagrams are efficient for model specification and often efficient in their evaluation. But it is difficult, if not impossible, to allow for dependencies (such as repair dependency and near-coincident-fault type dependency), transient and intermittent faults, standby systems with warm spares, and so on. Markov models can capture such important system behavior, but the size of a Markov model can grow exponentially with the number of components in this system. This paper presents an approach for avoiding the large state space problem. The approach uses a hierarchical modeling technique for analyzing complex reliability models. It allows the flexibility of Markov models where necessary and retains the efficiency of combinatorial solution where possible. Based on this approach a computer program called SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) has been written. The hierarchical modeling technique provides a very flexible mechanism for using decomposition and aggregation to model large systems; it allows for both combinatorial and Markov or semi-Markov submodels, and can analyze each model to produce a distribution function. The choice of the number of levels of models and the model types at each level is left up to the modeler. Component distribution functions can be any exponential polynomial whose range is between zero and one. Examples show how combinations of models can be used to evaluate the reliability and availability of large systems using SHARPE.     

Modular solution of dynamic multi-phase systems

Binary Decision Diagram (BDD)-based solution approaches and Markov chain based approaches are commonly used for the reliability analysis of multi-phase systems. These approaches either assume that every phase is static, and thus can be solved with combinatorial methods, or assume that every phase must be modeled via Markov methods. If every phase is indeed static, then the combinatorial approach is much more efficient than the Markov chain approach. But in a multi-phased system, using currently available techniques, if the failure criteria in even one phase is dynamic, then a Markov approach must be used for every phase. The problem with Markov chain based approaches is that the size of the Markov model can expand exponentially with an increase in the size of the system, and therefore becomes computationally intensive to solve. Two new concepts, phase module and module joint probability, are introduced in this paper to deal with the s-dependency among phases. We also present a new modular solution to nonrepairable dynamic multi-phase systems, which provides a combination of BDD solution techniques for static modules, and Markov chain solution techniques for dynamic modules. Our modular approach divides the multi-phase system into its static and dynamic subsystems, and solves them independently; and then combines the results for the solution of the entire system using the module joint probability method. A hypothetical example multi-phase system is given to demonstrate the modular approach.     

A separable method for incorporating imperfect fault-coverage intocombinatorial models

This paper presents a new method for incorporating imperfect FC (fault coverage) into a combinatorial model. Imperfect FC, the probability that a single malicious fault can thwart automatic recovery mechanisms, is important to accurate reliability assessment of fault-tolerant computer systems. Until recently, it was thought that the consideration of this probability necessitated a Markov model rather than the simpler (and usually faster) combinatorial model. SEA, the new approach, separates the modeling of FC failures into two terms that are multiplied to compute the system reliability. The first term, a simple product, represents the probability that no uncovered fault occurs. The second term comes from a combinatorial model which includes the covered faults that can lead to system failure. This second term can be computed from any common approach (e.g. fault tree, block diagram, digraph) which ignores the FC concept by slightly altering the component-failure probabilities. The result of this work is that reliability engineers can use their favorite software package (which ignores the FC concept) for computing reliability, and then adjust the input and output of that program slightly to produce a result which includes FC. This method applies to any system for which: the FC probabilities are constant and state-independent; the hazard rates are state-independent; and an FC failure leads to immediate system failure     

A methodology for reliability analysis in health networks

A reliability model for a health care domain based on requirement analysis at the early stage of design of regional health network (RHN) is introduced. RHNs are considered as systems supporting the services provided by health units, hospitals, and the regional authority. Reliability assessment in health care domain constitutes a field-of-quality assessment for RHN. A novel approach for predicting system reliability in the early stage of designing RHN systems is presented in this paper. The uppermost scope is to identify the critical processes of an RHN system prior to its implementation. In the methodology, Unified Modeling Language activity diagrams are used to identify megaprocesses at regional level and the customer behavior model graph (CBMG) to describe the states transitions of the processes. CBMG is annotated with: 1) the reliability of each component state and 2) the transition probabilities between states within the scope of the life cycle of the process. A stochastic reliability model (Markov model) is applied to predict the reliability of the business process as well as to identify the critical states and compare them with other processes to reveal the most critical ones. The ultimate benefit of the applied methodology is the design of more reliable components in an RHN system. The innovation of the approach of reliability modeling lies with the analysis of severity classes of failures and the application of stochastic modeling using discrete-time Markov chain in RHNs.     

Incorporating Common-Cause Failures Into the Modular Hierarchical Systems Analysis

This paper considers the problem of evaluating the reliability of hierarchical systems subject to common-cause failures (CCF); and dynamic failure behavior such as spares, functional dependence, priority dependence, and dependence caused by multi-phased operations. We present a separable solution that has low computational complexity, and which is easy to integrate into existing analytical methods. The resulting approach is applicable to Markov analyses, and combinatorial models for the modular analysis of the system reliability. We illustrate the approach, and the advantages of the proposed approach, through the detailed analyses of two examples of dynamic hierarchical systems subject to CCF.      

Parametric Analysis of 2-Unit Redundant Computer Systems With Corrective and Preventive Maintenance

This paper shows the span of results which can be obtained by modeling a system's behavior by stochastic processes and demonstrates practical rules for employing Markov and semi-Markov models. The introduction summarizes several methods for reliability analysis and gives the advantages and drawbacks of four methods: Markov processes, semi-Markov processes, supplementary variables, the method of stages. The remainder deals with reliability and availability modeling of a 2-unit redundant computer system. There are a) two types of maintenance: corrective (c. m.) and preventive (p. m.), and b) two system parameters: coverage, and an increased failure rate when one unit is under repair or inspection. Approximate expressions for reliability, mean time to failure, and asymptotic availability show the effects of the system parameters as well as of the shapes of the Cdf's of the times related to maintenance actions. For c.m., Markov modeling is a good approximation. For p.m., Markov modeling is a rough approximation; one can go to semi-Markov models or to the method of stages. Lastly, an approximate expression is given for the mean inspection interval which maximizes reliability and availability for p.m.     

A separable ternary decision diagram based analysis of generalized phased-mission reliability

This paper considers the reliability analysis of a generalized phased-mission system (GPMS) with two-level modular imperfect coverage. Due to the dynamic behavior & the statistical dependencies, generalized phased-mission systems offer big challenges in reliability modeling & analysis. A new family of decision diagrams called ternary decision diagrams (TDD) is proposed for use in the resulting separable approach to the GPMS reliability evaluation. Compared with existing methods, the accuracy of our solution increases due to the consideration of modular imperfect coverage; the computational complexity decreases due to the nature of the TDD, and the separation of mission imperfect coverage from the solution combinatorics. In this paper, the TDD-based separable approach is presented, and compared with existing methods for analyzing the GPMS reliability. An example generalized phased-mission system is analyzed to illustrate the advantages of our approach.     

Formulating and solving sequential decision analysis models withcontinuous variables

This paper presents a new decision analysis approach for modeling decision problems with continuous decision and/or random variables, and applies the approach to a research and development (R&D) planning problem. The approach allows for compact, natural formulation for classes of decision problems that are less appropriately addressed with standard discrete-variable decision analysis methods. Thus it provides a useful alternative analysis approach for problems that are often addressed in practice using simulation risk analysis methods. An illustrative application is presented to energy system R&D planning. The continuous-variable version of this model more directly represents the structure of the decision than a discrete approximation, and the resulting model can be efficiently solved using standard nonlinear optimization methods     

Power-hierarchy of dependability-model types

This paper formally establishes a hierarchy, among the most commonly used types of dependability models, according to their modeling power. Among the combinatorial (non-state-space) model types, we show that fault trees with repeated events are the most powerful in terms of kinds of dependencies among various system components that can be modeled. Reliability graphs are less powerful than fault trees with repeated events but more powerful than reliability block diagrams and fault trees without repeated events. By virtue of the constructive nature of our proofs, we provide algorithms for converting from one model type to another. Among the Markov (state-space) model types, we consider continuous-time Markov chains, generalized stochastic Petri nets, Markov reward models, and stochastic reward nets. These are more powerful than combinatorial-model types in that they can capture dependencies such as a shared repair facility between system components. However, they are analytically tractable only under certain distributional assumptions such as exponential failure- and repair-time distributions. They are also subject to an exponentially large state space. The equivalence among various Markov-model types is briefly discussed     

Markov模型在功能安全温度变送器的应用

针对如何分析功能安全产品的可靠性问题,提出了一种基于Markov模型的可靠性分析方法。通过对产品的系统结构进行分析,研究系统的 Markov状态转移图,结合失效模式、影响及其诊断分析(FMEDA)法对系统进行可靠性建模,引入状态转移矩阵,并以功能安全温度变送器为例对模型进行验证。相关验证结果表明,该模型应用于功能安全领域是可行的。     

Research of proactive complex event processing method

Based on the preliminary analysis results of the indeterminate event stream that generated by the sensors and control purpose equipment of CPS,the adaptive dynamic Bayesian network and parallel Markov decision process model were used to support the proactive complex event processing.In order to resolve the vast state space issue of Markov decision process for large CPS,states partition and reward decomposition methods were proposed to parallel the decision making process.The experimental result based on the simulation of traffic network shows that proposed method can process event stream effectively and has favorable scalability.     

Gain-adapted hidden Markov models for recognition of clean andnoisy speech

In applying hidden Markov modeling for recognition of speech signals, the matching of the energy contour of the signal to the energy contour of the model for that signal is normally achieved by appropriate normalization of each vector of the signal prior to both training and recognition. This approach, however, is not applicable when only noisy signals are available for recognition. A unified approach is developed for gain adaptation in recognition of clean and noisy signals. In this approach, hidden Markov models (HMMs) for gain-normalized clean signals are designed using maximum-likelihood (ML) estimates of the gain contours of the clean training sequences. The models are combined with ML estimates of the gain contours of the clean test signals, obtained from the given clean or noisy signals, in performing recognition using the maximum a posteriori decision rule. The gain-adapted training and recognition algorithms are developed for HMMs with Gaussian subsources using the expectation-minimization (EM) approach     

Approximate sensitivity analysis for acyclic Markov reliability models

Acyclic Markov chains are frequently used for reliability analysis of nonmaintained mission-critical computer-based systems. Since traditional sensitivity (or importance) analysis using Markov chains can be computationally expensive, an approximate approach is presented which is easy to compute and which performs quite well in test cases. This approach is presented in terms of a Markov chain which is used for solving a dynamic fault-tree, but the approach applies to any acyclic Markov reliability model.     

基于Markov链Packet-Level的VANET差错预测模型及性能预估

 车用自组织网络—VANET(Vehicle Ad-hoc Network)是一种应用于智能交通系统的新型无线移动自组织网络,其网络体系及各层协议设计与信道特性紧密相关,信道实时测量与性能预估逐渐成为VANET通信协议设计基础.本文主要思想是通过实测数据包的差错序列来评估VANET信道质量.首先通过实测数据分析,发现差错序列中相邻无误串和错误串具有统计依赖关系;然后提取数据包的差错序列统计特性参数,依此建立基于Markov链的PLE(Packet-Level Error)模型;最后通过不同模型仿真结果与实际统计结果的比较与验证,PLE模型比传统的Gilbert-Elliott模型更适合描述VANET信道特征.     

Reliability Modeling of Hardware and Software Interactions, and Its Applications

We classify system failures into three categories: hardware failures, software failures, and hardware-software interaction failures. We develop a unified reliability model that accounts for failures in all three categories. Hardware, and software failures are accounted for with well-known modeling approaches. In this paper, we propose a modeling methodology using Markov processes to capture hardware-software interaction failures. We illustrate the combined hardware & software modeling approach by applying it to a real telecommunication system     

Reliability modeling of hardware/software systems

This paper uses a single model to analyze the effects of both hardware and software on system reliability. A unified model of hardware and software reliability is developed using Markov modeling. Then the effect of hardware and software failures is studied using the model. The model incorporates concepts from both hardware and software reliability modeling. Examples of both simplex (nonredundant) and redundant architectures are analyzed using the model     

Combinatorial methods for the evaluation of yield and operational reliability of fault-tolerant systems-on-chip

In this paper we develop combinatorial methods for the evaluation of yield and operational reliability of fault-tolerant systems-on-chip. The methods assume that defects are produced according to a model in which defects are lethal and affect given components of the system following a distribution common to all defects; the method for the evaluation of operational reliability also assumes that the fault-tree function of the system is increasing. The distribution of the number of defects is arbitrary. The methods are based on the formulation of, respectively, the yield loss and the operational unreliability as the probability that a given Boolean function with multiple-valued variables has value 1. That probability is computed by analyzing a ROMDD (reduced ordered multiple-value decision diagram) representation of the function. For efficiency reasons, a coded ROBDD (reduced ordered binary decision diagram) representation of the function is built first and, then, that coded ROBDD is transformed into the ROMDD required by the methods. We present numerical experiments showing that the methods are able to cope with quite large systems in moderate CPU times.     

Failure correlation in software reliability models

Perhaps the most stringent restriction in most software reliability models is the assumption of statistical independence among successive software failures. The authors research was motivated by the fact that although there are practical situations in which this assumption could be easily violated, much of the published literature on software reliability modeling does not seriously address this issue. The research work in this paper is devoted to developing the software reliability modeling framework that can consider the phenomena of failure correlation and to study its effects on the software reliability measures. The important property of the developed Markov renewal modeling approach is its flexibility. It allows construction of the software reliability model in both discrete time and continuous time, and (depending on the goals) to base the analysis either on Markov chain theory or on renewal process theory. Thus, their modeling approach is an important step toward more consistent and realistic modeling of software reliability. It can be related to existing software reliability growth models. Many input-domain and time-domain models can be derived as special cases under the assumption of failure s-independence. This paper aims at showing that the classical software reliability theory can be extended to consider a sequence of possibly s-dependent software runs, viz, failure correlation. It does not deal with inference nor with predictions, per se. For the model to be fully specified and applied to estimations and predictions in real software development projects, we need to address many research issues, e.g., the detailed assumptions about the nature of the overall reliability growth, way modeling-parameters change as a result of the fault-removal attempts     

分布式媒体服务系统结合请求调度的接入控制研究

由于多媒体业务需要更大的带宽和更高的实时性,所以对服务系统和接入控制提出了更高的要求。文章针对分布式媒体服务系统提出了一种新颖的接入控制方法,与以往方法不同之处在于将请求调度融合在接入控制之中,从而提高了系统的服务性能。我们先为系统建立部分可观Markov决策过程(POMDP)模型,并将请求调度融合在决策中,然后使用基于观测的随机接入控制策略,通过策略梯度优化算法仿真求解模型的最优策略。仿真结果表明,与其他分布式接入控制方法相比,该文所提方法在有效利用系统资源的同时,提高了系统性能。