An improved algorithm for the symbolic reliability analysis of networks

Increasing reliability at a minimum amount of extra cost is a major challenge in todays ECU network design. Considering reliability as an objective already in early design phases has the potential to avoid expensive modifications in later design phases. Hence, there is a need for an appropriate optimization process and efficient analysis techniques to evaluate the found implementations. In this paper, we will show how symbolic techniques can be used to efficiently analyze and optimize such reliable systems. The contribution of this paper is (1) a symbolic reliability analysis that makes use of a partitioned structure function and (2) a symbolic optimization process based on binary ILP solvers. Our case study from the automotive area will show a significant speed-up using our analysis technique. Moreover, our optimization approach is able to offer implementations with considerably improved reliability at no additional costs as well as implementations with reduced costs without decreasing their reliability.

The authors describe an efficient Boolean algebraic algorithm to compute the probability of a union of nondisjoint sets as applied to symbolic reliability analysis. Coherent networks and fault-trees with statistically-independent components characterized by their minimal pathsets or cutsets are used as examples for generating the nondisjoint sets. The algorithm uses the concept of multiple variable inversion originally proposed by A. Grnarov et al. (1979). The authors illustrate improvements in the use of the multiple variable inversion technique for this problem using two examples. The algorithm is extended to compute the reliability importance of a given component (sensitivity of system reliability to the component reliability). A computer program implementing the modified algorithm is used to solve and obtain measured time complexities for a large set of network and fault tree models. >

ABSTRACTIn the process of reliability analysis of computer network, there is the high probability of network failure in the application process by using the current analysis method. In this paper, a reliability analysis method of computer network based on intelligent cloud computing method is proposed. The proposed method is integrated with the theoretical idea of analytic hierarchy process. From the research angle of network reliability, the reliability model of computer network is set up by using the failure criterion of computer network, and the intelligent cloud computing method is used to calculate the reliability state parameters of the network. According to the obtained state parameters of network reliability analysis, the reliability analysis matrix of computer network is established, and then to accurately complete the reliability analysis of computer network. The simulation results show that the reliability analysis method of computer network based on intelligent cloud computing is highly accurate and robust.

A new and efficient algorithm for topological formulation of network functions in symbolic form for a general linear (passive or active) network is presented. This algorithm, which is based on the concept of graph partitioning developed by Dunn and Chan, has all of the advantages of the existing topological formulas, such as overcoming the problem of sign evaluation and greatly reducing the number of term cancellations and the number of topological terms. In addition, and perhaps the most important, it presents a step-by-step procedure for formulating network functions that can readily be adapted for computer implementation. Finally, it is shown that the theoretical results developed herein are most suited for sensitivity analysis of linear active networks.

A new method for reliability analysis of stochastic flow networks is proposed. Nodes as well as branches can have limited capacities and can fail. The method builds a branching tree. The tree-nodes are the disjoint terms of a symbolic reliability expression. The paper shows how to find the probability function and mean of the network performance. The method is both simple and effective. The minimal paths or cutsets are not needed explicitly. On a computer the memory effort is very low #x0005F; it increases only quadratically with the number of failing elements. The method can be modified reasonably for other reliability problems.

In this paper, we propose an algorithm to construct the ordered binary decision diagram (OBDD) representing the cut function of a terminal-pair network. The algorithm recognizes isomorphic sub-problems and thus avoids redundant computations. The system reliability could be efficiently computed by the OBDD. Finally, we propose an approach to compute the importance measures for multiple components by traversing the OBDD only once. The correctness and the effectiveness of our approach are demonstrated by experiments on 30 benchmark networks. The experimental results on a 2-by-100 lattice network, which has 2/sup 99/ paths or 10,000 cuts, show an impressive improvement compared to the previous works using the sum of disjoint products method that have exponential complexity. The CPU time of our method, including the calculation of not only the reliability but also the importance measures, for a 100-stage lattice network is only about 0.24 seconds. Thus, this approach is very helpful for the reliability and sensitivity analysis of large networks.

While most of the research efforts on network reliability analysis have focused on networks with imperfect links but perfectly reliable nodes, binary decision diagram–based methods have recently been developed to determine reliability of networks with imperfect links and imperfect nodes. The smaller the binary decision diagram size is, the better the performance of the binary decision diagram–based algorithm will be. The binary decision diagram size heavily depends on the chosen variable ordering. As finding the optimal variable ordering is a nondeterministic polynomial time-complete problem, a heuristic is typically used. Various heuristics based on depth-first search, breadth-first search, and network-driven search have been developed for network reliability analysis. However, they often assume the networks have perfectly reliable nodes. This article advances the state-of-the-art by proposing a new ordering heuristic for the binary decision diagram–based reliability analysis of networks with imperfect links/edges and nodes. Empirical studies show that the proposed new ordering heuristic can generate significantly smaller binary decision diagram size for a wide variety of network structures than the traditional depth-first search–based, breadth-first search–based, network-driven search–based heuristics for most cases, enabling efficient binary decision diagram–based reliability analysis of large-scale networks considering both link and node failures. Robustness of the proposed heuristic is also investigated through empirical studies.

Recent earthquake events evidenced that damage of structural components in a lifeline network may cause prolonged disruption of lifeline services, which eventually results in significant socio‐economic losses in the affected area. Despite recent advances in network reliability analysis, the complexity of the problem and various uncertainties still make it a challenging task to evaluate the post‐hazard performance and connectivity of lifeline networks efficiently and accurately. In order to overcome such challenges and take advantage of merits of multi‐scale analysis, this paper develops a multi‐scale system reliability analysis method by integrating a network decomposition approach with the matrix‐based system reliability (MSR) method. In addition to facilitating system reliability analysis of large‐size networks, the multi‐scale approach enables optimizing the level of computational effort on subsystems; identifying the relative importance of components and subsystems at multiple scales; and providing a collaborative risk management framework. The MSR method is uniformly applied for system reliability analyses at both the lower‐scale (for link failure) and the higher‐scale (for system connectivity) to obtain the probability of general system events, various conditional probabilities, component importance measures, statistical correlation between subsystem failures and parameter sensitivities. The proposed multi‐scale analysis method is demonstrated by its application to a gas distribution network in Shelby County of Tennessee. A parametric study is performed to determine the number of segments during the lower‐scale MSR analysis of each pipeline based on the strength of the spatial correlation of seismic intensity. It is shown that the spatial correlation should be considered at both scales for accurate reliability evaluation. The proposed multi‐scale analysis approach provides an effective framework of risk assessment and decision support for lifeline networks under earthquake hazards. Copyright © 2009 John Wiley & Sons, Ltd.

Reliability analysis of a logic network is considered in this paper. A logic network has some special properties that are not very common in reliability engineering. Firstly, the structure function of a logic network depends not only on the work of its components (logic gates), but also on the values of the input signals. Secondly, a logic network is a typical type of noncoherent system. These two characteristics imply that its reliability analysis is more complicated than other systems. In this paper, we study the structure function of a logic network and propose a definition of the Structural Importance Measure (SIM) for this type of systems. The definition of the SIM is based on Logical Differential Calculus.

The possibility of applying the measure of information uncertainty (entropy) in structural reliability analysis of electricity distribution networks is assessed. The essential factors influencing the process of reliability analysis and related to the presence of information uncertainty are highlighted. The peculiarities of reliability analysis in network designing and operation, when it is required to estimate the state of elements and choose the efficient structure of a network are considered. The opportunities and limitations in the application of existing methods of structural reliability analysis are evaluated; it proves the importance of information uncertainty measurement. The conceptual apparatus of entropy includes the mathematical procedure of its calculation. Fail-safe operation probability and probability of network element failure are taken as reliability index. The mathematical expressions proposed for calculating entropy are applicable when there are two independently functioning network elements, each of which will be able to be in one of two states with a fraction of probability: operable and non-operable. The obtained results allowed identifying the more reliable elements/objects. The application of the proposed mathematical procedure allows to compare network elements with each other through the value of entropy and to choose the most reliable ones. Thus, the proposed solution is a preliminary step in the uncertainty removal task.

Recently, computation, communication and control are deeply and pervasively embedded into physical and engineered components of network System, to advance entire networked system performance and dependability. In such systems, the growing complexity of network structure result in need of increasing system reliability. Network reliability modeling and analysis need not only capture the complex dynamic process of network systems, but also need the facilities for applying, understanding and computing. The objective of this paper is to develop a suitable modeling and analysis framework to study the ad-hoc network reliability. Using the mutual transformations of reliability functions between the time-domain and the frequency-domain, we adopt frequency-domain analysis approach to model network reliability. The advantages of the proposed approach over the traditional time-domain approaches include capability to capture higher order moments of system characteristics, scalability to analyze reliability of complex network systems, efficiency in calculation and practicability in computer simulation, etc. Finally, numerical experiment results show the feasibility and efficacy of this approach.

The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The detectability levels are estimated from the twenty-five years of recorded data in terms of 50%, 90% and 100% cumulative detectability thresholds, which were derived from frequency-magnitude distribution. From this analysis the 100% level of detectability of the network is M L=1.7 for events which occur within the network. The accuracy in hypocentral solutions of the network is investigated by considering the fixed real hypocenter within the network. The epicentral errors are found to be less than 4 km when the events occur within the network. Finally, the problems faced during continuous operation of the local network, which effects its detectability, are discussed.

To analyze the influence of distribution network with grid-connected photovoltaic (PV) generation on the power supply reliability, in this paper it firstly regards interconnected PV generation as an equivalent generator with rated capacity as well as the island operation mode of PV to set up a model for reliability calculation and analysis. Based on the network equivalent method, the structure of distribution system with PV is simplified and then the reliability indexes of distribution system are worked out based on Failure Mode and Effects Analysis (FMEA). At last, a comparative calculation between the distribution network with incorporated PV generations and that without PV generations is made. After analyzing a real example, the results suggest that integrating PV power generations reasonably into the distribution network can enhance the reliability of whole distribution system.

With the increase of distributed generation(DG) in distribution networks, the impact of these DGs in the power grid is also increasingly significant. In particular, with the development of intermittent DG which is more difficult to predict power generation, it challenges the reliability of the power grid. According to the characteristics of the grid with DG, this paper simplifies the distribution network to a system which takes autonomous control units(ACU) as the main component. Then the power grid is divided and classified into autonomous control regions(ACR) and calculates the reliability indices of different classes of ACR. And through the reliability index to establish an optimization model, eventually configure the system's DG capacity and improving the system's reliability.

During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios.