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1.
Reliability Modeling Using SHARPE   总被引:1,自引:0,他引:1  
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.  相似文献   

2.
Power-hierarchy of dependability-model types   总被引:1,自引:0,他引:1  
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  相似文献   

3.
This paper proposes an imperfect-repair model for repairable systems where two repair modes, perfect and minimal, occur in accordance with a Markov chain. It investigates the characteristics of the model and presents a statistical procedure for estimating the lifetime distribution of the system, based on consecutive inter-failure times. Under the Brown-Proschan imperfect repair model, the system is repaired to: good-as-new under perfect-repair, its “condition just prior to failure” under minimal-repair. This imperfect-repair model generalizes the Brown-Proschan imperfect-repair model, by allowing first-order dependency between two consecutive repair modes. The model assumes that, at failure, the system undergoes either perfect repair (restore to like new) or minimal repair (restore to like “just before failure”), and the repair modes are subject to a Markov process. The estimation procedure is developed in a parametric framework for incomplete data where some repair modes are not recorded. The s-expectation-maximization principle is used to address the incomplete-data problem. Under the assumptions that the lifetime distribution belongs to a parametric family having aging property and explicit form of the survival function, an algorithm is developed for finding the MLE (maximum likelihood estimates) of the reliability parameters; the transition probabilities of the repair modes, as well as the distribution parameters. A Monte Carlo study shows the consistency, effect of aging rate, effect of transition types, and effect of missing data, for the estimates  相似文献   

4.
郭洋洋  宋月  李刚平 《电子科技》2013,26(1):131-134,137
故障模式并非一种,并且部件也不可能修复如新,因此研究了多状态不可修复如新的n-1/n(G)系统的可靠性。在部件的寿命和维修时间均服从指数分布情况下,采用补充变量法以及广义马尔科夫过程理论,得到了多状态系统的瞬时可用度,可靠度等可靠性指标的Laplace变换表达式以及系统首次故障前的平均时间,并以3中取连续2好系统为例说明了已得结论的实用价值。  相似文献   

5.
基于小波域统计建模及显著性修正的SAR图像相干斑抑制   总被引:1,自引:0,他引:1  
该文提出了一种基于小波域统计建模与小波系数显著性修正相结合的斑点噪声滤波方法.这种方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,对对数变换后的图像进行小波变换并对小波域的高频子带系数用混合高斯模型与隐马尔可夫树模型进行建模,并采用EM算法来估计模型参数.在模型参数估计的基础上;利用贝叶斯最小均方误差准则来估计"干净"的小波系数.在此基础上引入基于显著性准则的小波系数修正,最后通过小波逆变换与指数变换获得抑制斑点噪声后的图像.用真实SAR图像实验表明,该文提出的方法能够有效地抑制斑点噪声,同时能够很好地保存边缘细节结构与强散射中心.  相似文献   

6.
It is proposed that computable mathematical models of physiological systems can be explained and studied by a query language based upon converting the source code into a relation database scheme. In such a scheme, each assignment statement is assumed to be equivalent to a functional dependency. The dependencies formed constitute a lossless decomposition of the model scheme when the derived dependencies are augmented by a key consisting of input attributes. The method is demonstrated on two common computational forms: simultaneous equations and the Euler solution to ordinary differential equations. A simple example is used to show that computing closure on attributes can establish the difference between two models.  相似文献   

7.
Graphical models, such as Bayesian networks and Markov random fields (MRFs), represent statistical dependencies of variables by a graph. The max-product “belief propagation” algorithm is a local-message-passing algorithm on this graph that is known to converge to a unique fixed point when the graph is a tree. Furthermore, when the graph is a tree, the assignment based on the fixed point yields the most probable values of the unobserved variables given the observed ones. Good empirical performance has been obtained by running the max-product algorithm (or the equivalent min-sum algorithm) on graphs with loops, for applications including the decoding of “turbo” codes. Except for two simple graphs (cycle codes and single-loop graphs) there has been little theoretical understanding of the max-product algorithm on graphs with loops. Here we prove a result on the fixed points of max-product on a graph with arbitrary topology and with arbitrary probability distributions (discrete- or continuous-valued nodes). We show that the assignment based on a fixed point is a “neighborhood maximum” of the posterior probability: the posterior probability of the max-product assignment is guaranteed to be greater than all other assignments in a particular large region around that assignment. The region includes all assignments that differ from the max-product assignment in any subset of nodes that form no more than a single loop in the graph. In some graphs, this neighborhood is exponentially large. We illustrate the analysis with examples  相似文献   

8.
Joint source-channel turbo decoding of entropy-coded sources   总被引:1,自引:0,他引:1  
We analyze the dependencies between the variables involved in the source and channel coding chain. This analysis is carried out in the framework of Bayesian networks, which provide both an intuitive representation for the global model of the coding chain and a way of deriving joint (soft) decoding algorithms. Three sources of dependencies are involved in the chain: (1) the source model, a Markov chain of symbols; (2) the source coder model, based on a variable length code (VLC), for example a Huffman code; and (3) the channel coder, based on a convolutional error correcting code. Joint decoding relying on the hidden Markov model (HMM) of the global coding chain is intractable, except in trivial cases. We advocate instead an iterative procedure inspired from serial turbo codes, in which the three models of the coding chain are used alternately. This idea of using separately each factor of a big product model inside an iterative procedure usually requires the presence of an interleaver between successive components. We show that only one interleaver is necessary here, placed between the source coder and the channel coder. The decoding scheme we propose can be viewed as a turbo algorithm using alternately the intersymbol correlation due to the Markov source and the redundancy introduced by the channel code. The intermediary element, the source coder model, is used as a translator of soft information from the bit clock to the symbol clock  相似文献   

9.
Signals arising out of nonlinear dynamics are compelling models for a wide range of both natural and man-made phenomena. In contrast to signals arising out of linear dynamics, extremely rich behavior is obtained even when we restrict our attention to one-dimensional (1-D) chaotic systems with certain smoothness constraints. An important class of such systems are the so-called Markov maps. We develop several properties of signals obtained from Markov maps and present analytical techniques for computing a broad class of their statistics in closed form. These statistics include, for example, correlations of arbitrary order and all moments of such signals. Among several results, we demonstrate that all Markov maps produce signals with rational spectra, and we can therefore view the associated signals as “chaotic ARMA processes,” with “chaotic white noise” as a special case. Finally, we also demonstrate how Markov maps can be used to approximate to arbitrary accuracy the statistics any of a broad class of non-Markov chaotic maps  相似文献   

10.
In this paper, we propose a new language model, namely, a dependency structure language model, for information retrieval to compensate for the weaknesses of unigram and bigram language models. The dependency structure language model is based on the first‐order dependency model and the dependency parse tree generated by a linguistic parser. So, long‐distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model, where the dependency structure model gives a better performance than recently proposed language models and the Okapi BM25 method, and the dependency structure is more effective than unigram and bigram in language modeling for information retrieval.  相似文献   

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