Purpose The purpose of this paper is to evaluate various reliability measures like reliability, expected lifetime (mean time to failure), signature reliability and compare networks based on the different flows. Design/methodology/approach The reliability characteristics of complex bridge networks have been evaluated using different algorithms with the help of universal generating function (UGF). Further, the signature reliability of the considered networks has been determined using Owen’s method. Findings The present paper proposes an efficient algorithm to compute the reliability indices of complex bridge networks having i.i.d. lifetime components (nodes, edges) with the help of UGF and Owen’s method. This study reveals that a slight change in the complex bridge network affects the reliability significantly. Finally, by the reliability structure function, proposed algorithms are used to find the signature and MTTF. From signature, we have determined the different failure probabilities corresponding to edges of the network. Originality/value In this work, we have evaluated reliability characteristics and signature reliability of the complex bridge networks using UGF method and Owen’s method respectively unlike done in the past.

Wind energy is a kind of renewable energy that plays a significant role in remote areas for pumping water. The windmill is also used to generate electricity. The windmill is also known as a wind pump when it is used for pumping water. In this work, the authors proposed a complex hybrid model of an example of combined system (windmill, rechargeable battery and pumping system) to evaluate the system’s performance. System performance was affected by system degradation due to system failure. These factors also affected the profit of the user. Two types of repair facilities for continuous and satisfactory performance of the system were assumed. To illustrate the system modeling using the Gumbel–Hougaard family of the copula, numerical examples were used for the exploration of Markov results of the reliability measures and the profit of the system with the warranty period, with this also being demonstrated graphically.

In this study, we calculate hesitant and dual hesitant fuzzy reliability of linear and circular consecutive 2-out-of-4:G system from reliability function and Weibull distribution. After that, we introduced some basic methods for investigating the fuzzy reliability in the form of upper and lower membership and non- membership function having an aggregation operator with equal weights. At last, two numerical examples are also illustrated with the considered technique.

PurposeThe present paper focuses on the evaluation of reliability and its characteristics (Mean time to failure and Sensitivity) of a k-out-of-n network.Design/methodology/approachThe minimal cuts of the network have been evaluated for different nodes in this paper, using an algorithm. With the help of these cuts, reliability and its characteristics are obtained using Gumbel–Hougaard family of the copula.FindingsThe present paper proposes to compute the reliability and its measures of the k-out-of-n network using the minimal cuts and copula methodology. The completely failed nodes of the network have been repaired using Gumbel–Hougaard family of the copula.Originality/valueIn this paper, the reliability of a k-out-of-n network has been evaluated by first calculating k-out-of-n minimal cuts, and the failed nodes have also been repaired using Gumbel–Hougaard family of the copula, unlike as done in the past.

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Network reliability is one of the most important concepts in this modern era. Reliability characteristics, component significance measures, such as the Birnbaum importance measure, critical importance measure, the risk growth factor and average risk growth factor, and network reliability stability of the communication network system have been discussed in this paper to identify the critical components in the network, and also to quantify the impact of component failures. The study also proposes an efficient algorithm to compute the reliability indices of the network. The authors explore how the universal generating function can work to solve the problems related to the network using the exponentially distributed failure rate. To illustrate the proposed algorithm, a numerical example has been taken.