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Existing research has been concentrated on improving the reliability of a distributed computing system through optimizing tasks allocation, providing software redundancy and providing hardware redundancy. None of these works considered the performance sharing mechanism in a distributed computing system. Different from other performance sharing systems whose reliability can be calculated directly, the reliability evaluation of a distributed computing system with performance sharing is more challenging since the reliability depends on the task execution time of each processor after performance sharing. This research considers a distributed computing system with performance sharing mechanism such that the computing power can be redistributed among different processors in the system. A reliability model is proposed to evaluate the distributed computing system with performance sharing. An optimization model is formulated to derive the optimal performance sharing policy such that the system reliability can be maximized. Both analytic examples and numerical examples are carried out to illustrate the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations. [ABSTRACT FROM AUTHOR]

As transport is a vital component in the modern tourism industry, determining reliable-performance tourism transport networks is critical for tour operators and travel agencies. The performance of a tourism transport network in this study is investigated by evaluating system reliability, which is the probability of successfully transporting requested passengers between multiple origins and multiple destinations within a time constraint when tardiness may occur. A stochastic multiple-origin-destination tourism transport network with tardiness (SMTNT) is formulated for system reliability evaluation. Identify all minimal travel paths (MTP) and corresponding maximum tardiness vectors that successfully connect each origin–destination pair under a time constraint. System reliability is derived from all flow vectors enabling delivery via MTPs a requested number of passengers at different tardiness levels. The proposed algorithm is successful in handling a combination of time constraints in the case of tardiness with multiple origin–destination pairs. Furthermore, it helps travel agencies obtain an insight into the performance of their SMTNT. An example describing the working of the proposed algorithm with an analysis of system reliability is provided. The results of the study can be used for improving the algorithm in the future. [ABSTRACT FROM AUTHOR]

This article proposes an uncertainty analysis method for evaluating the reliability of a system and calculating the relative importance of the system inputs when the probabilities of basic events are characterized by intervals. The reliability requirement or the safety criterion is first considered in the system reliability assessment. A nonprobabilistic reliability index, called R-index, is defined to evaluate the reliability of the system. Two importance measure indices, called location effect and size effect, are introduced for estimating the influence of the probability uncertainties of basic events on the R-index, thus identifying influential and noninfluential basic events and comparing their relative importance. Then, the proposed method is applied to an illustrative fault tree example and the fault tree analysis of unilateral asymmetric movement of an aircraft flap mechanism, thereby explaining the significance of this method. The results show that the proposed method provides a viable tool for system reliability and importance measure analysis when probability intervals are involved. [ABSTRACT FROM AUTHOR]

A reliability evaluation method is proposed for production systems by applying the capacitated-flow production network (CFPN) model. In particular, this article considers in-line stockers to monitor and to avoid blockage and starvation in the CFPN. First, the minimal capacity that all cells (group of machines) should provide to meet demand is generated. Second, status of stocker is monitored via the proposed “stocker volume check table” to calculate the volume usage of a stocker, and the stocker nonutilization is then obtained. Finally, the system reliability is derived in terms of the minimal capacity vector and stocker nonutilization. An important contribution of this article is to avoid complicated dependency calculation when multiple stockers are considered. A practical case of printed circuit board production is further studied to illustrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The working conditions of multicomponent systems are usually dynamic and stochastic. Reliability evaluation of such systems is challenging, since the components are generally positively correlated. Based on the cumulative exposure principle, we model the effects of the dynamic environments on the component lifetimes by a common stochastic time scale, and exponential dispersion process is utilized to describe the stochastic time scale. Then, the component lifetimes are shown to be positively quadrant dependent, and the joint survival function of the component lifetimes is derived, which includes the results of  as special cases. In this article, we show that neglecting either the effects of dynamic environments or the correlation among component lifetimes would underestimate the reliability of series systems and overestimate the reliability of parallel systems. We also investigate the problem of parameter redundancy of the model, and give some suggestions for data analysis. Simulation studies show that the unified model is flexible and useful for suggesting an optimal model given observed data. [ABSTRACT FROM AUTHOR]

Obtaining lower confidence bounds on the reliability of a series system remains a problem of interest. Engineers and practitioners desire methods with good properties to obtain lower confidence bounds when only component-level binomial test data are available. With numerous methods proposed in the literature–none of which are uniformly superior–it can be an overwhelming task to select the method which best handles the scenario at hand. We develop a software tool in R (available in the “serieslcb” package) through which users can discover the methods that best suit their situation. The tool runs user-defined simulations and then ranks the best performing methods based on objective comparisons utilizing a delta coverage metric. This article outlines the methods and comparison strategy implemented in the package. It also discusses the design of the software tool and illustrates it with an example. [ABSTRACT FROM AUTHOR]

This paper considers the optimal arrangement problem in a connected- $(r,s)$ -out-of- $(m,n)$ :F lattice system. This problem aims to find a component arrangement to maximize system reliability under the assumption that the component reliabilities are given. Although existing studies developed a few algorithms for finding the optimal arrangement of connected- $(r,s)$ -out-of- $(m,n)$ :F lattice systems, these existing algorithms can only find the optimal arrangement in the special cases. In this paper, we develop an improved algorithm for efficiently finding the optimal arrangement of general connected- $(r,s)$ -out-of- $(m,n)$ :F lattice systems. It is based on the branch-and-bound method. First, we derive three conditions for pruning a branch based on the existing conditions for the optimal arrangement. The conditions can reduce the number of candidates for the optimal arrangement. Moreover, we improve the algorithm by incorporating the derived conditions as given by Omura et al. In the improved algorithm, we compute the system reliability efficiently by memorizing the specific values and then utilizing them. The result of the numerical experiment shows the proposed algorithm is more efficient than the algorithm proposed by Omura et al. in terms of the computation time. The result shows that the proposed algorithm can find the optimal arrangement whereas the algorithm proposed by Nakamura et al. cannot find. The optimal arrangement and its system reliability, which the proposed algorithm provides can also be used to evaluate the quality of solutions obtained by (meta) heuristic algorithms. [ABSTRACT FROM AUTHOR]

Reliability importance which serves to quantify the influence of each component (or each type of components) in each phase on the reliability of a phased mission system (PMS) plays an important role in security assessment and risk management. In this paper, we present a new and efficient method for reliability importance analysis of PMSs using the theory of survival signature. A new kind of survival signature is applied to assess the reliability of PMS with multiple types of components. A closed-form formula is derived to predict reliability importance of the PMS with respect to each type of components in each phase. The Birnbaum importance model is further extended to calculate the reliability importance of the PMS with respect to each component in each phase. Finally, two numerical examples are used to demonstrate the validity and effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Components in a system work under the same dynamic operating conditions, and their lifetimes are generally positively correlated. Ignorance of the correlation may lead to a significant bias in the evaluation of system reliability. Using the cumulative exposure principle, we model the equivalent operating time of the components, resulting from the cumulative effects of the dynamic environments, as a monotone increasing stochastic time scale. Commonly-used models, such as the compound Poisson, gamma, and the inverse Gaussian processes, are adopted for the stochastic time scale. Based on the above settings, reliability models for multicomponent systems are developed. We investigate how the stochastic time scale influences the system reliability and the correlations between component lifetimes. Under the stochastic time scale, the component lifetimes are shown to be positively quadrant dependent. Overlook of the correlation would overestimate the reliability of a parallel system but underestimate the reliability of a series system. When the stochastic time scale degenerates to a deterministic function of the calendar time, on the other hand, the system reliability becomes the reliability of the system where components work independently. The proposed models are successfully applied to lifetime data of brake pads in the automobile braking system. [ABSTRACT FROM AUTHOR]

A great deal of previous work has studied methods to determine system reliability. That work has defined system reliability as the probability that the production output meets a predetermined demand for a network with many workstations, each of which has random capacity determined by a discrete probability distribution. It is noteworthy that the archival work presents numerous examples, wherein entities are discrete and indivisible, while the analysis is based upon continuous flow, much like a fluid, through the network. The inconsistency, inherent in mixing discrete-entity examples with continuous-flow analysis, can result in erroneous conclusions about the system reliability. The current paper presents a rigorous discrete-analysis analytical approach, which is called the Song rule (Stochastic “Output $\geq$ demand” Networks and their Generation). Based on all examples studied in this paper, the absolute difference ratios between the previous incorrect (as published) and the correct probabilities (from the Song rule) are all greater than 23%. The proposed Song rule analysis is verified to be correct using discrete-event simulation. In addition to providing rigorous analysis for the network problems under consideration, the Song rule is a useful tool for assessing the validity of any future proposed approach for other stochastic reliability problems. [ABSTRACT FROM PUBLISHER]

A new modeling approach is presented to optimally and simultaneously design the configuration of a multicomponent system and determine a maintenance plan with uncertain future stress exposure. Traditionally, analytical models for system design and maintenance planning are applied sequentially, but this new model provides an integrated approach to make decisions considering the lifecycle cost of the system. Specifically considering the influence of uncertain future usage stresses on component and system reliability, the integrated redundancy allocation and maintenance planning problem is formulated as a two-stage stochastic programming model with recourse. In this model, the system is exposed to uncertain usage scenarios with their associated probabilities of occurrence or likelihood. The decision variables for the first stage are the selection of component types and the number of components to be used in the system, and these variables are modeled before the uncertainty is revealed. The second-stage variables, involving a recourse function, are the preventive maintenance plan, which defines optimal maintenance times for planned replacement of components under distinct usage scenarios. Numerical examples and sensitivity analysis on series–parallel systems demonstrate applications of the proposed model and provide further insights. The comparisons of the proposed integrated approach to traditional sequential method show advantages of the proposed model in cost saving. [ABSTRACT FROM PUBLISHER]

Statistical decision rules have been used successfully in various engineering fields for many years: system reliability, quality control, start-up tests, and switching rules. Many of these decision rules are made of multiple-run-rules with their statistical distributions and characteristics that remain largely unknown. This paper shows that, given multiple-run-rules, there exists a single run-rule defined by a compound pattern which is equivalent to the multiple-run-rules and its waiting time is finite Markov chain imbeddable. Their distributions and causation probabilities can be obtained through the finite Markov chain imbedding (FMCI) technique which provides ways to obtain theoretical and numerical results for many probabilistic engineering systems problems. Two examples of multiple-run-rules, one in system reliability and the other in quality control, are provided to demonstrate the imbedding method and theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we give and discuss a general multilinear expression of the structure function of an arbitrary semicoherent system in terms of its minimal path and cut sets. We also examine the link between the number of minimal path and cut sets consisting of one or two components and the concept of structure signature of the system. [ABSTRACT FROM AUTHOR]

Cascading failure models have been studied more recently as for electric power systems, epidemics, and interdependent networks. The current paper presents three cascading failure models in the point-view of system reliability based on the normalized CASCADE model. The corresponding system reliability indices are developed including system reliability, expectation lifetimes of systems, distributions of total number of failures, distributions of cascading failure propagation generations, and other related probabilities. Two numerical examples are discussed, and the conclusions are summarized. [ABSTRACT FROM AUTHOR]

For a hybrid flow shop (HFS), due to the maintenance, partial failure, and possibility of failure, the number of machines at a workstation presents multiple levels, meaning that capacity of each workstation is stochastic. This study considers the stochastic capacity of each workstation and proposes a performance index based on system reliability to measure the probability that the HFS can complete demand d within time constraint T. The HFS is modeled as a stochastic-flow network, in which each arc is regarded as a workstation with stochastic capacity and each node as a buffer. An algorithm is then developed to find the lower capacity vectors that satisfy (d, T) and system reliability is evaluated. In addition, a practical example of an IC card manufacturing system is utilized to illustrate the proposed algorithm and show that such an index can provide enough information for managers to make decisions. [ABSTRACT FROM AUTHOR]

Enabling more than one function in a component provides a new cost-effective way to develop a highly reliable system. In this paper, we study the reliability of a one-shot system containing multifunctional components. Such systems have attracted increasing attention in many areas, such as ad hoc sensor networks. We derive the expressions for system reliability and reliability of each function, and formulate a redundancy allocation problem (RAP) with the objective of maximizing system reliability. Unlike constructing a system with single-functional components, the number of copies of a specific function to be included in each multifunctional component (i.e., functional redundancy) needs to be determined as part of the design. Moreover, a start-up strategy for turning on specific functions in these components must be decided prior to system operation. We develop a heuristic algorithm and include it in a two-stage genetic algorithm (GA) to solve the new RAP. We also apply a Tabu search (TS) method for solving such NP-hard problems. Our numerical studies illustrate that the two-stage GA and the TS method are quite effective in searching for high-quality solutions. [ABSTRACT FROM AUTHOR]

A consecutive-k-out-of- n : F ( G ) system with sparse d consists of n components ordered in a line or a circle, while the system fails (works) iff, there exist at least k consecutive failed (working) components with sparse d for 0 \le d \le n - k . In this paper, a circular consecutive- k -out-of- n system with sparse d is considered. Some equations for system reliability and Birnbaum importance are derived by means of the finite Markov chain imbedding approach. Then the Birnbaum importance of components is compared in the situations where the system is under an IID model, and where one of the components is known to be failed, respectively. Finally, some numerical examples are followed to illustrate the results obtained in the paper. [ABSTRACT FROM PUBLISHER]

Statistical inference of the component lifetime distribution is developed when Type-II censored system lifetime data are observed with a known system structure. The component lifetime distributions are assumed to be from either the log-location-scale family of distributions or the location-scale family of distributions. Two estimation methods, the maximum likelihood method, and the regression-based method, are proposed for the model parameters, and the corresponding computational formulae are provided. Construction of confidence intervals for the model parameters is also considered. The methodologies are illustrated with two commonly used lifetime distributions: the Weibull, and the lognormal. Monte Carlo simulations are used to study the performances of the point and interval estimation methods proposed here. Finally, some recommendations are made based on the obtained simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes a performance indicator to evaluate the capability of a maintainable stochastic-flow network (MSFN). This MSFN is required to preserve a minimal service level so that it can send d units of data or commodity from the source to the sink through multiple paths within time T. The proposed system reliability performance indicator quantifies the probability that a MSFN delivers a capacity level above the minimal service level with a budget no greater than B. Two procedures are integrated in the proposed algorithm: a procedure to estimate system reliability, and an adjusting procedure that utilizes the branch-and-bound approach for exact system reliability. The estimated system reliability with lower and upper bounds, and the exact system reliability, are then computed by applying the recursive sum of disjoint products (RSDP) algorithm. [ABSTRACT FROM AUTHOR]

This paper generalizes the linear multistate consecutively-connected system model by introducing allowable gaps. The new model consists of N+1 linearly ordered nodes. Some of these nodes contain statistically independent multistate elements with different characteristics. Each element j can provide a connection between the node to which it belongs and Xj next nodes, where Xj is a discrete random variable with known probability mass function. The system fails if it contains at least m consecutive nodes not connected with any previous node (m consecutive gaps). An algorithm based on the universal generating function method is suggested for the system reliability evaluation. Illustrative examples are presented. [ABSTRACT FROM PUBLISHER]

The problem of fault diagnosis has been discussed widely, and the diagnosability of many well-known networks has been explored. Strong diagnosability, and conditional diagnosability are both novel measurements for evaluating reliability and fault tolerance of a system. In this paper, some useful sufficient conditions are proposed to determine strong diagnosability, and the conditional diagnosability of a system. We then apply them to show that an n-dimensional augmented cube AQn is strongly (2n-1)-diagnosable for n\geq 5, and the conditional diagnosability of AQn is 6n-17 for n\geq 6. Our result demonstrates that the conditional diagnosability of AQn is about three times larger than the classical diagnosability. [ABSTRACT FROM PUBLISHER]

System reliability optimization models considering uncertainty are described, and new algorithms are proposed to minimize the coefficient of variation of the system reliability estimate. This is a realistic, important formulation because the reliability of most components is not known with certainty, and decision-makers are often risk averse, preferring a system with a slightly lower estimated reliability if it is a more certain measure. The redundancy allocation problem can be defined as the selection of the system configuration and the type of the components to optimize some objective function(s) while satisfying some system related constraints. In this paper, we propose algorithms to minimize the coefficient of variation of the system reliability estimate with respect to a minimum system reliability constraint, and some other system related constraints. Two algorithms are presented. For problems where component mixing is not allowed, an optimal algorithm is presented based on linear integer programming. For problems where mixing is allowed, a heuristic approach is presented based on a combined neighborhood search, and a linear integer programming approach. The heuristic starts with the solution of those problems where mixing components is not allowed, and searches the neighborhood of this solution to find better prospective solutions for the problems where mixing components is allowed. Then, a linear integer programming problem is solved to identify the recommended solution to the problem. [ABSTRACT FROM AUTHOR]

In 1986, Fu proposed the finite Markov chain imbedding approach. Since then, it has played an important role in the analyses of system reliability, such as on reliability computations, importance analyses of components, failure rate function computations, the birth of new reliability system models, and analyses of new reliability tests. We present a survey on the developments and applications of this approach in the reliability field. [ABSTRACT FROM PUBLISHER]

Reliability estimation of High Performance Computing (HPC) systems enables resource allocation, and fault tolerance frameworks to minimize the performance loss due to unexpected failures. Recent studies have shown that compute nodes in HPC systems follow a time varying failure rate distribution such as Weibull, instead of the exponential distribution. In this paper, we propose a model for the Time to Failure (TTF) distribution of a system of k s-independent nodes when individual nodes exhibit time varying failure rates. We also present the system reliability, failure rates, Mean Time to Failure (MTTF), and derivations of the proposed system TTF model. The model is validated using observed data on time to failure. [ABSTRACT FROM AUTHOR]

A hierarchical decomposition procedure is proposed to determine the variance of the reliability estimate for complex systems with duplicated components. For these systems, multiple copies of the same component type are used within the system, but only a single reliability estimate is available for each distinct component type. The variance of the reliability estimate is magnified at the system-level due to the covariance of component reliability estimates. Estimating the covariance becomes a formidable task if the system structure is complicated. A hierarchical model is proposed to decompose the system reliability estimate into component levels through intermediate layers. The decomposition procedure causes reliability estimates of duplicated components to remain s-independent when computing the associated variance on the adjacent upper layer. The first order Taylor series expansion is used to propagate the variance from the component level to the system level via intermediate layers. The hierarchical decomposition is preferable for designing robust, reliable systems by reducing or minimizing the system reliability variance at the component level. [ABSTRACT FROM AUTHOR]

We consider the problem of maximizing the reliability of a series-parallel system given cost and weight constraints on the system. The number of components in each subsystem, and the choice of components are the decision variables. In this paper, we propose an integer linear programming approach that gives an approximate feasible solution, close to the optimal solution, together with an upper bound on the optimal reliability. We show that integer linear programming is a useful approach for solving this reliability problem. The mathematical programming model is relatively simple. Its implementation is immediate by using a mathematical programming language, and integer linear programming software. And the computational experiments show that the performance of this approach is excellent based on a comparison with previous results. [ABSTRACT FROM AUTHOR]

Systems which must be designed to achieve very low probabilities of failure often use redundancy to meet these requirements. However, redundant k-out-of-n:G systems which are subject to imperfect fault coverage have an optimum level of redundancy, nopt. That is to say, additional redundancy in excess of nopt will result in an increase, not a decrease, in the probability of system failure. This characteristic severely limits the level of redundancy considered in the design of highly reliable systems to modest values of n. Correct modeling of imperfect coverage is critical to the design of highly reliable systems. Two distinctly different k-out-of-n:G imperfect fault coverage reliability models are discussed in this paper: Element Level Coverage (ELC), and Fault Level Coverage (FLC). ELC is the appropriate model when each component can be assigned a given coverage level, while FLC is the appropriate model for systems using voting as the primary means of selection among redundant components. It is shown, over a wide range of realistic coverage values and relatively high component reliabilities, that the optimal redundancy level for ELC systems is 2 and 4 for FLC systems. It is also shown that the optimal probability of failure for FLC systems exceeds that of ELC systems by several orders of magnitude. New functions for computing the mean time to failure for i.i.d. k-out-of-n:G ELC, and FLC systems are also presented. [ABSTRACT FROM AUTHOR]

Systems requiring very high levels of reliability, such as aircraft controls or spacecraft, often use redundancy to achieve their requirements. This paper provides highly efficient techniques for computing the reliability of redundant systems involving simple k-out-of-n arrangements, and those involving complex structures which may include imbedded k-out-of-n structures. Techniques for modeling systems subject to imperfect fault coverage must be appropriate to the redundancy management architecture utilized by the system. Systems for which coverage can be associated with each of the redundant components, perhaps taking advantage of the component's built-in test capability, are modeled with what we term Element Level Coverage (ELC); while systems which utilize majority voting for the selection from among redundant components are modeled with Fault Level Coverage (FLC). In FLC, systems coverage is a function of the fault sequence, i.e., coverage will be greater for the initial faults which can utilize voting for redundant component selection, but will have a lower coverage value when the system must select from among the last two operational components. Occasionally, FLC systems can be adequately modeled using a simplified version of FLC in which it can be assumed that the initial fault coverage values are unity. This model is called one-on-level coverage (OLC). The FLC algorithms provided in this paper are of particular importance for the correct modeling of systems which utilize voting to select from among their redundant elements. While combinatorial, and recursive techniques for modeling ELC, FLC, and OLC have been previously reported, this paper presents new table-based algorithms, and binary decision diagram-based algorithms for these models which have superior computational complexity. The algorithms presented here provide the ability to analyse large, and complex systems very efficiently, in fact with a computational complexity comparable to the best available techniques for systems with perfect fault coverage. [ABSTRACT FROM AUTHOR]

We introduce the system of consecutive failures with sparse d which is a natural extension of consecutive-k systems. Then a series of generalizations of consecutive-k systems are discussed, such as consecutive-k-out-n:F systems with sparse d, M consecutive-k-out-of-n:F systems with sparse d, and (n, f, k) :F systems with sparse d. We present the formulation for the system reliability of these generalized consecutive-k systems with various component settings in terms of the finite Markov Chain imbedding idea, along with two numerical examples. [ABSTRACT FROM AUTHOR]

Systems requiring very high levels of reliability, such as aircraft controls or spacecraft, often use redundancy to achieve their requirements. Reliability models for such redundant systems have been widely treated in the literature. These models describe k-out-of-n:G systems, where n is the number of components in the system, and k is the minimum number of components that must work if the overall system is to work. Most of this literature treats the perfect fault coverage case, meaning that the system is perfectly capable of detecting, isolating, and accommodating failures of the redundant elements. However, the probability of accomplishing these tasks, termed fault coverage, is frequently less than unity. Correct modeling of imperfect coverage is critical to the design of highly reliable systems. Even very high values of coverage, only slightly less than unity, will have a major impact on the overall system reliability when compared to the ideal system with perfect coverage. The appropriate coverage modeling approach depends on the system design architecture, particularly the technique(s) used to select among the redundant elements. This paper demonstrates how coverage effects can be computed, using both combinatorial, and recursive techniques, for four different coverage models: Perfect Fault Coverage (PFC), Element Level Coverage (ELC), Fault Level Coverage (FLC), and One-on-one Level Coverage (OLC). The designation of PFC, ELC, FLC, and OLC to distinguish types of coverage modeling is suggested in this paper. [ABSTRACT FROM AUTHOR]

An m-consecutive-k-out-of-n:F system, introduced by W.S. Griffith, consists of an ordered linear sequence of n i.i.d. components that fails if there are at least m non-overlapping runs of k consecutive failed components. However, a situation may occur in which an ordered linear sequence of n i.i.d. components fails if there are at least m non-overlapping runs of at least k consecutive failed components. We call such a system an m-consecutive-at least-k-out-of-n:F system. This paper presents a graphical evaluation and review technique (GERT) analysis of both types of systems providing closed form explicit formulae for reliability evaluation in a unified manner. GERT, besides providing a visual picture of the system, helps to analyse the system in a less inductive manner. Numerical examples for each system are studied in detail by computing the reliability for various combinations of sets of values of the parameters involved. It is observed that m-consecutive-at least-k-out-of-n:F systems are more reliable than m-consecutive-k-out-of-n:F systems as the number of possible state combinations leading to system's failure are larger in the latter. Mathematica is used for systematic computations. Numerical investigations illustrate the efficiency of GERT in reliability analysis of such systems. In comparison with the existing formulae of m-consecutive-k-out-of-n:F systems for i.i.d. components, the formula obtained by GERT analysis, to be referred to as GERT-F, is much more efficient owing to its significantly low computational time, and easy implementation. [ABSTRACT FROM AUTHOR]

A new heuristic is proposed and tested for system reliability optimization. The multiple weighted objective heuristic is based on a transformation of the problem into a multiple objective optimization problem, and then ultimately, transformation into a different single objective problem. The multiple objectives are to simultaneously maximize the reliability of each individual subsystem. This is a logical approach because system reliability is the product of the subsystem reliabilities, so if they are maximized, the system reliability will also be high. This new formulation and associated heuristic are then based on solving a sequence of linear programming problems. It is one of the very few optimization approaches that allow for linear programming algorithms and software to be used for the redundancy allocation problem when mixing of functionally equivalent components is allowed. Thus, it represents an efficient solution method that relies on readily available optimization tools. The heuristic is tested on many example problems, and compared to competing solution approaches. Overall, the heuristic performance is observed to be very good on the tested problem, and superior to the max-mm heuristic regarding both efficiency, and performance. [ABSTRACT FROM AUTHOR]

In this paper, we study the system reliability optimization of distributed access networks subject to a constraint on the total cost. We first formulate the cost-constrained system reliability optimization problem as a searching process in a combinatorial tree, which enumerates all the possible solutions to the problem. Because the calculation of each possible solution for the reliability problem is extremely time-consuming, a novel algorithm, the Shrinking & Searching Algorithm (SSA), is proposed to speed up the searching process. SSA jointly considers the upper bound of the system reliability for each branch in the combinatorial tree, and the cost constraint on the possible solutions. It avoids most of the redundant calculations in the searching process by gradually shrinking the difference between lower & upper bounds of the length of a path in the corresponding combinatorial tree, which represents a feasible solution. Case study & simulation results are presented to demonstrate the performance of the SSA. [ABSTRACT FROM AUTHOR]

General failure event data from various sources are often used to estimate the failure probability for the system of interest, especially when s-dependence exists among component failures, where common cause failure plays an important role. Failure event data from different sources must be reasonably explained, and correctly applied, so that the information about load environment, and component/system property can be used correctly. In estimating the probability for s-dependent system failure, both the load distribution, and component strength distribution are much more important than component failure probability index. Based on the relationship among different multiple failures, this paper presents a data mapping approach to estimating dependent system failure probability through multiple failure event data of other systems with different sizes. The underlying assumption on data mapping is that failures of different multiples (including single) are correlated with each other for a group of components if they are subjected to the same or correlated random load (loads). Taking the situation of a group of s-independent components operating under the same random load as an example, the likelihood of a component failure at a trial depends not only on the strength of the individual component but also on the realization of the random load. The likelihood of a specific multiple failure at a trial is also determined by both the component strengths, and the realization of the random toad. Further- more, if a larger load sample appears, the likelihoods for failure are higher. Conversely, if a smaller load sample appears, the likelihoods of failure are lower. We emphasized in this paper that system failure event data should be interpreted & applied under the principle that various multiple failures are distinguished by their respective failure multiplicity and/or system size, and are inherently interrelated through correlated load environments. The approach starts with determining the load parameter, and component strength parameter according to multiple (including single) failure event data available. Then, these parameters are used to calculate the probability of multiple failures for systems of different sizes. This approach is applicable to predict high multiple failure probability based on low multiple failure event data. Examples of estimating multiple failure probabilities of EDG (emergency diesel generators) with mapped data illustrate that the proposed approach is desirable. [ABSTRACT FROM AUTHOR]

Systems subjected to imperfect fault-coverage may fail even prior to the exhaustion of spares due to uncovered component failures. This paper presents optimal cost-effective design policies for k-out-of-n:G subsystems subjected to imperfect fault-coverage. It is assumed that there exists a k-out-of-n: G subsystem in a nonseries-parallel system and, except for this subsystem, the redundancy configurations of all other subsystems are fixed. This paper also presents optimal design polices which maximize overall system reliability. As a special case, results are presented for k-out-of-n:G systems subjected to imperfect fault-coverage. Examples then demonstrate how to apply the main results of this paper to find the optimal configurations of all subsystems simultaneously. In this paper, we show that the optimal n which maximizes system reliability is always less than or equal to the it which maximizes the reliability of the subsystem itself. Similarly, if the failure cost is the same, then the optimal n which minimizes the average system cost is always less than or equal to the n which minimizes the average cost of the subsystem. It is also shown that if the subsystem being analyzed is in series with the rest of the system, then the optimal n which maximizes subsystem reliability can also maximize the system reliability. The computational procedure of the proposed algorithms is illustrated through the examples. [ABSTRACT FROM AUTHOR]

Summary and Conclusions - This paper presents four models for optimizing the reliability of embedded systems considering both software and hardware reliability under cost constraints, and one model to optimize system cost under multiple reliability constraints. Previously, most optimization models have been developed for hardware-only or software-only systems by assuming the hardware, if any, has perfect reliability. In addition, they assume that failures for each hardware or software unit are statistically independent. In other words, none of the existing optimization models were developed for embedded systems (hardware and software) with failure dependencies. For our work, each of our models is suitable for a distinct set of conditions or situations. The first four models maximize reliability while meeting cost constraints, and the fifth model minimizes system cost under multiple reliability constraints. This is the first time that optimization of these kinds of models has been performed on this type of system. We demonstrate and validate our models for an embedded system with multiple applications sharing multiple resources. We use a Simulated Annealing optimization algorithm to demonstrate our system reliability optimization techniques for distributed systems, because of its flexibility for various problem types with various constraints. It is efficient, and provides satisfactory optimization results while meeting difficult-to-satisfy constraints. [ABSTRACT FROM AUTHOR]

Summary and Conclusions-System reliability evaluation for flow networks is an important issue in quality management. This paper concentrates on a stochastic-flow network in which nodes as well as branches have several possible capacities, and can fail. The possibility is evaluated that a given amount of messages can be transmitted through the stochastic-flow network under the budget constraint. Such a possibility, system reliability, is a performance index for a stochastic-flow network. A minimal path, an order sequence of nodes & branches from the source to the sink without cycles, is used to assign the flow to each component (branch or node). A lower boundary point for (d, C) is a minimal capacity vector, which enables the system to transmit d messages under the budget C. Based on minimal paths, an efficient algorithm is proposed to generate all lower boundary points for (d, C). The system reliability can then be calculated in terms of all lower boundary points for (d, C) by applying the inclusion-exclusion rule. Simulation shows that the implicit enumeration (step 1) of the proposed algorithm can be executed efficiently. [ABSTRACT FROM AUTHOR]

Summary and Conclusion - With recent advances in network technologies and computing power, multimedia systems have become a popular means for information delivery. However, testing of these systems is difficult. Due to incomplete control of their runtime and communication environment, precise temporal properties of multimedia systems are nonreproducible. Traditional software testing, which mainly deals with functional correctness, cannot be directly applied to testing temporal properties. Furthermore, time points are hard to be measured exactly, and in this sense are nondeterministic and nonreproducible. To address this problem, we propose a framework for testing the generic temporal properties of media objects in distributed multimedia software systems (DMSS). The timing properties are based on Allen's basic binary temporal relations between two objects, which can be extended to cover multiple objects. We have developed techniques for test case generation, and test result analysis based on a distributed tester architecture. Test templates are used in test case generation to reduce the possibility of human error, and the entire testing procedure can be automated. A prototype system has been built to test a DEC HPAS multimedia presentation system, which is a multimedia system supporting W3C's SMIL standard. Detailed discussions on practical issues illustrated with a number of actual tests are given. Experimental results have shown that our framework is effective in detecting errors in temporal properties. Furthermore, ways to reduce the test effort have been discussed, and guidelines for coming up with criteria for verdict computation based on the realtime requirements of the applications have been suggested. [ABSTRACT FROM AUTHOR]