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In this paper, we propose a new fault tolerant multistage interconnection network (MIN) and a new adaptive self-routing scheme for the network. It can provide more multiple paths than the related previous networks between an input/output pair of a network by adding extra links between switching elements in the same stage and modifying the self-routing scheme of the banyan network. Our routing scheme is as simple as that of the banyan network, which is based on the topological relationships among the switching elements (SE’s) that render a packet to the same destination with the regular self-routing, which are discovered in this paper. We present an algebraic proof to show the correctness of this scheme, and an analytic reliability analysis to provide quantitative comparisons with other networks, which shows that the new network is more cost effective than the banyan network and other augmented MIN’s in terms of the reliability.

Nodes usually cooperate to form groups and survive or fail in real-world systems. Researchers typically consider the interdependence between node groups in studying the interdependent network. This paper studies the robustness of interdependent hypergraph networks under different attack strategies. According to the characteristics of the network model, we propose a series of target attack strategies and compare the destructive effect of these strategies on the network. Second, we analyze the impact of the random edge removal strategy on the robustness of hypergraph networks under different edge removal ratios. Finally, we propose four target-node edge removal strategies and compare their destructive effects on the network at the same edge removal ratios. Simulation results show that target attack and edge removal strategies can appreciably reduce the robustness of interdependent hypergraph networks and accelerate the networks’ collapse.

Critical constituent gates are first detected and graded based on their individual impact of an error in the outputs. This brings in the idea of practical reliability analysis metric. Then, the approximation of arithmetic circuits by random logic applied to least significant gates is introduced. The 74283 fast adder is used as an example to illustrate the feasibility of the proposed methods. Simulation results show the potential efficient application of the proposed reliability analysis metric and approximation method.

In recent years, the structure of multi-phase buck converter also called Interleaved Buck Converter (IBC) has gained considerable attention. The advantages of the IBC in comparison to the conventional Buck converter (CBC) are the lower output current ripple, higher efficiency, fast transient response, lower electromagnetic interference and higher reliability. Since more than one stage is employed in the IBC, this converter is highly reliable. In this paper, the reliability and mean time to failure (MTTF) of the CBC, and two- and three-stage IBCs are figured out. Using the obtained results and considering various scenarios, a comprehensive comparison is provided. In addition, the operation of the converter in case of fault occurrence for high and low capacities is analyzed and reliability is evaluated in each state. The relation between the reliability and temperature of semiconductor elements is discussed. Furthermore, a laboratory-scaled prototype is used to extract the experimental results of the temperature variation of the elements during a fault. Markov model is used to evaluate the analyzed reliability.

Correctly measuring the reliability and availability of a cloud-based system is critical for evaluating its system performance. Due to the promised high reliability of physical facilities provided for cloud services, software faults have become one of the major factors for the failures of cloud-based systems. In this paper, we focus on the software aging phenomenon where system performance may be progressively degraded due to exhaustion of system resources, fragmentation and accumulation of errors. We use a proactive technique, called software rejuvenation, to counteract the software aging problem. The dynamic fault tree (DFT) formalism is adopted to model the system reliability before and during a software rejuvenation process in an aging cloud-based system. A novel analytical approach is presented to derive the reliability function of a cloud-based Hot SPare (HSP) gate, which is further verified using Continuous Time Markov Chains (CTMC) for its correctness. We use a case study of a cloud-based system to illustrate the validity of our approach. Based on the reliability analytical results, we show how cost-effective software rejuvenation schedules can be created to keep the system reliability consistently staying above a predefined critical level.

In this paper we consider the problem of reliability modeling and analysis of hierarchical computer-based systems (HS) with modular imperfect coverage (MIPC) and common-cause failures (CCF). The MIPC and CCF can cause vertical dependence that runs through different levels of the system as well as horizontal dependence that runs across components or modules on the same system level. The consideration of these dependencies poses unique challenges to existing HS reliability analysis methods. We propose an efficient decomposition and aggregation approach named EDA-HS to the reliability evaluation of complex hierarchical systems with both MIPC and CCF as one way to meet the above challenges in an efficient and elegant manner. Our approach is to decouple the effects of both MIPC and CCF from the combinatorics of the solution. The approach is represented in a dynamic fault tree by a proposed probabilistic functional dependency gate and a proposed CCF gate modeled after the existing FDEP gate. We present the basics and advantages of the EDA-HS approach by working through an analysis of an example HS subject to MIPC and CCF.

Many offshore oil and gas installations in the North Sea are approaching the end of their designed lifetimes. Technological improvements and higher oil prices have developed favorable conditions for more oil recovery from these existing installations. However, in most cases, an extended oil production period does not justify investment in new installations. Therefore cost-effective maintenance of the existing platform infrastructure is becoming very important.

In this paper, an inspection frequency optimization model has been developed which can be used effectively by the inspection and maintenance personnel in the industry to estimate the number of inspections/optimum preventive maintenance time required for a degrading component at any age or interval in its lifecycle at a minimum total maintenance cost. The model can help in planning inspections and maintenance intervals for different components of the platform infrastructure. The model has been validated by a case study performed on flowlines installed on the top side of an offshore oil and gas platform in the North Sea. Reliability analysis has been carried out to arrive at the best inspection frequency for the flowline segments under study.

Modeling correlated component failures poses a unique challenge for reliability researchers because it requires ingenuity to devise an approach free from the assumption that components fail in a statistically independent manner. Several studies have addressed this problem with models that introduce additional parameters to describe the correlated failure of components. However, these earlier techniques often require the correlations to be positive and almost always introduce an exponential number of correlation parameters. These restrictions limit the scalability of existing approaches for conducting sensitivity analysis on the correlation parameters, which could identify correlation reductions that would improve system reliability. This paper presents a technique for reliability and sensitivity analysis that requires only a quadratic number of correlation parameters, encompassing systems with both negative and positive component correlations. Unlike previous research, the proposed approach places no unnecessary restrictions on a system's correlation parameters. A series of examples illustrates the flexibility of the approach. The results quantitatively confirm that negative component correlation assists fault-tolerant systems to attain levels of reliability even higher than systems of statistically independent redundant components. Thus, the techniques introduced here offer a methodology to concisely measure the utility of negative component correlations on system reliability improvement.

Many sampling-based approaches are currently available for calculating the reliability of a design. The most efficient methods can achieve reductions in the computational cost by one to several orders of magnitude compared to the basic Monte Carlo method. This paper is specifically targeted at sampling-based approaches for reliability analysis, in which the samples represent calls to expensive finite element models. The aim of this paper is to illustrate how these methods can further benefit from reduced order modeling to achieve drastic additional computational cost reductions, in cases where the reliability analysis is carried out on finite element models. Standard Monte Carlo, importance sampling, separable Monte Carlo and a combined importance separable Monte Carlo approach are presented and coupled with reduced order modeling. An adaptive construction of the reduced basis models is proposed. The various approaches are compared on a thermal reliability design problem, where the coupling with the adaptively constructed reduced order models is shown to further increase the computational efficiency by up to a factor of six.

The importance of reliability to complex systems cannot be disputed as they are the backbones of our society. In practice, the common cause failures may have severe reverse function on complex systems’ overall stability. Survival Signature opens a new way to perform reliability analysis on systems with multiple component types. This paper under takes a research on survival signature-based reliability analysis on complex systems susceptible to Common Cause Failures. To be specific, it proposes the standard $\alpha$-factor model and general $\alpha$-factor model to combine with the survival signature. In practical applications, the $\alpha$-factor estimator of the system might not be defined completely due to limited data, or knowledge which requires to take imprecision into account. Some numerical cases are presented to show the applicability of the methods for complex systems. In addition, this paper may attract people’s attention on the conception of Design for Reliability.

In this work, a methodology that uses the dynamic Bayesian networks (DBNs) in combination with an idea algebra is developed for assessing the dynamic reliability of engineering systems. A network representation of the system topology is first introduced in the form of “idea” objects representing components and their functional interfaces, thus integrating the functional and material descriptions of the system. Various time-dependent functionalities can thus be mapped to segments or loops of the resulting network, which are then translated automatically into the form of a DBN, thereby avoiding the need to manually generate the dynamic fault tree (DFT) logic that would normally serve as a starting point. The methodology is demonstrated in a case study, where reliability analysis of an automobile system is performed. The idea algebra is automatically deployed in Mathematica and evaluated in the GeNIe platform. Weibull distribution was used for the generation of the dynamic values for the reliability analysis of the system within a certain period.

The feeding unit is an important functional part in the paper industry that requires the development of effective maintenance plan based on its reliability analysis. This study analyzes reliability of the considered unit employing trapezoidal fuzzy numbers in fuzzy $\lambda$–$\tau$ methodology with Petri net modeling. In this analysis, the collected crisp input data of each subsystem has been fuzzified using trapezoidal fuzzy numbers. Then fuzzy values of several reliability factors of feeding unit, namely, failure rate, repair time, reliability, availability, etc. at ±15%, ±25% and ±40% spread levels have been calculated. Further, the obtained fuzzy values of reliability factors have been defuzzified to analyze system behavior of feeding unit. The analysis is useful for plant maintenance and operation management to understand the behavior of feeding unit in a more realistic manner.

This paper presents a new four-parameter lifetime model by generalizing the Inverse Weibull (IW) distribution using Transmuted Kumaraswamy family of distributions. The proposed model explains the high probability at the tail effectively. The induction of additional parameters enhance the potentiality of the IW distribution and make it a pliant model that provides a vast range of existing distributions as special cases. Several probabilistic properties of the proposed model including moments, probability weighted moments, moment generating function, quantile function, reliability measures, and order statistics are discussed. The maximum likelihood (ML) procedure is adopted for the estimation of model parameters. The efficiency of ML estimates is to testify through simulation study. Four datasets from the field of reliability science are used to expound the competence of the proposed model.

This study discusses the reliability and sensitivity analyses of a retrial machine repair system with warm standby units and a single repair server which can take multiple vacations when there is no failed unit in the system under the recovery policy. All the random periods, including failure times of operating and standby units, the repair service times, retrial times, breakdown times of the single server, and repair times of the breakdown server are assumed to have exponential distributions and independent to each other. The state rate transition diagram is utilized to derive the differential equation of each state probability, and then this study takes the Laplace transform on both sides of the differential equation. After solving the Laplace transform, system reliability and mean time to system failure can be derived. Extensive numerical examples are designed and executed to determine how system reliability, mean time to system failure and sensitivity analysis are affected by the change of each system parameter.

In this paper we develop a pipeline processing system called NMR pipeline. In an NMR pipeline, processing elements at each stage are replicated to achieve high fault tolerance. Each stage of the pipeline is an N-Modular Redundant (NMR) System. This design combines the benefits of pipeline processing and NMR systems and is suitable for high speed safety-critical computing. Reliability analysis of this system is presented taking into account possible failures of processing and communication elements. The optimality of the system is defined in terms a suitably defined cost function that accounts for the component costs and the costs associated with system unreliability.

This paper is concerned with the effect of various data recording errors on the estimation of parameters in models commonly used in the analysis of reliability data. We start by outlining sources of such errors, and propose some modeling strategies that allow these errors into the framework of our analysis. It is then shown that the estimation of model parameters needs to take into account a mis-specified model, with the consequence that any theoretical advantages nominally enjoyed by estimators are reduced. In particular, the results from a series of simulation experiments show that the maximum likelihood estimator is no longer asymptotically unbiased. We next outline an approach that generalizes the usual asymptotic theory to obtain expressions for both the mean and variance of the maximum likelihood estimator in the present framework; these expressions involve both the underlying distribution and parameters controlling the extent to which recording errors are present in a data set. We then link these expressions to results obtained in a series of simulation experiments, and show that this approach accommodates a general formulation of the effect of data recording errors. We conclude with a discussion of the practical consequences of this work.

Accurate analysis of the reliability of a system requires that all major variations in the system's operation are accounted for. Most reliability analyses assume that the system's configuration, success criteria, and components' behavior remain the same during an entire mission. However, system requirements and behavior vary over the time. Moreover, individual components may be repaired (if failed) such that repairs do not conflict with the operation of the system. Thus repairs are independent of the system state. Thus, multiple phases are natural. For repairable systems, Markov analysis techniques are used but they suffer from state space explosion. This limits the size of system that can be analyzed and it is expensive in computation. We present a new computationally efficient technique for the analysis of phased-mission systems where the operational states of a system can be described by combinations of component states (such as fault trees or assertions). We avoid the state space explosion. The phase algebra is used to account for the effects of variable configurations, repairs, and success criteria from phase to phase. Our technique yields exact (as opposed to approximate) results. We demonstrate our technique by means of several examples and present numerical results to show the effects of phases and repairs on the system reliability/availability.

This paper presents the reliability analysis of the frame structures with semi-rigid connections. For this purpose, the SEMIFEM finite element program that is capable of dealing with the semi-rigid connections is coded in FORTRAN. Then, this program is connected to the reliability algorithm. The direct coupling method, which is a combination of the reliability method and finite element method, is utilized to determine the reliability indexes and probabilities of failure for the structure. The first order reliability method (FORM) is the one favored in the present reliability analysis. Two sets of steel framed structures are analyzed; each of four and eight stories, consisting of a portal frame and three types of concentrically braced frames. Concrete compression strength limit state in reinforced concrete (RC) columns, steel strength limit state in steel braces and inter-story drift limit state are considered in reliability evaluation. According to the limit states, X braced frames are determined as the safest structures, while the portal frames are regarded as the most unsafe structures. As the connection percentage increases, the safety of the structure increases in terms of inter-story drift and steel strength limit states, but decreases for concrete compression strength limit states.

This study introduces closed-form formulation for estimating wind load effects with various mean recurrence intervals (MRIs), which was originally derived for assessing probabilistic seismic performance of structures, but has not been adopted in wind engineering community. The accuracy of the closed-form formulation is verified by comparing its estimations with those from traditional numerical integration for a wide range of probabilistic responses of both rigid and flexible structures. The closed-form formulation sheds more physical insights regarding the influence of uncertainties in wind speed and extreme response coefficient. It results in a more convenient and accurate definition of design response for a target MRI over the widely used Cook–Mayne coefficient in wind engineering. With this closed-form framework, the influence of epistemic uncertainties attributed to limited knowledge and data is also quantified in terms of confidence interval of estimation in closed-form formulation.

Displacement- and reliability-based designs of tuned mass damper (TMD) for a shear building are studied herein. Different sources of uncertainties such as earthquake records and their peak ground accelerations (PGA), masses of floors, cross-sectional dimensions of structural members, damping of the structure and modulus of elasticity are considered. Monte Carlo simulation (MCS) is used for evaluating the performance of the designed TMD. A method for generating artificial earthquake record by using wavelet packet transform (WPT) and particle swarm optimization (PSO) is proposed to generate artificial records for areas without sufficient strong ground motion records. An illustrative example is used to study the displacement- and reliability-based designs of TMD, which are related to minimizing the structural displacement and maximizing the performance of TMD, respectively. In addition, the performance of TMD on mitigating the response of structure and its reliability under uncertain parameters of loading and structural properties are investigated. The results show that a displacement-based designed TMD could reduce the lateral displacement of a structure. Furthermore, it illustrates that the reliability-based designed TMD has a better performance in real condition of loading and structural parameters.