Reliability indices and signature analysis of complex networks
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Date
2018-08
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
The present research is based on the development of reliability models with the application of UGF, Supplementary variable technique, ANN, and Signature. In this study, six different reliability models have been developed. Model 1 generates 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. Applying reliability structure function, proposed algorithms are used to find the signature and the MTTF. Model 2 assess the reliability
characteristics of bridge network and square ladder network on the basis of the flow of information/signals from a source node to a sink node using the universal generating function. The main focus of this model is to estimate the reliability indices, namely, reliability, MTTF, sensitivity assuming failure to be exponentially distributed. Furthermore, for computing the signature of networks with the help of Owens method we have assumed that all components are independent and have identically distributed. Model 3 deals with the reliability and signature
analysis of one of the most common multistage interconnection networks, namely, Shuffle exchange networks (SENs).An efficient algorithm has been established to compute the signature and Mean-time-to-failure of shuffle exchange networks with the help of Owen’s method
incorporating independent and identically distributed lifetime component. All perspectives of the reliability, viz. terminal, broadcast and network reliability have been analyzed with the help of universal generating function. Model 4 estimates the reliability of Benes network having both independent and identically and non-identically distributed components in three contexts, viz. terminal, broadcast, and network with the help of universal generating function. Subsequently, the signature is computed with the help of Owen’s method using different algorithms. At last, we have computed the mean time to failure with the help of minimal signature. Model 5 focuses on the reliability indices and signature evaluation of all-digital protection system. In this model, firstly, we have computed the reliability of all-digital protection systems using a universal generating function having both independent and identically and non-identically distributed components. Afterwards, the signature is evaluated with the help of Owen’s method using different algorithms where all the system components are coherent. Lastly, we have calculated the mean time to failure with the help of minimal signature. Model 6 [Section 1] introduces the concept of the acyclic transmission network in which numbers of nodes are capable of receiving or sending a signal to the target nodes. The model combined the concepts of Markov processes and minimal cuts incorporating copula to find the various reliability measures. Various reliability characteristics
such as transition state probabilities, reliability, MTTF and sensitivity of the proposed network has been evaluated with the help of minimal cuts coupling with Markov processes using Gumbel-Hougaard copula, supplementary variable techniques and Laplace transforms. [Section 2] studies the reliability and profit function of acyclic transmission network with the application of Markov process and artificial neural network. After determining the reliability by Markov process, it is improved with the help of the artificial neural network. All the presented models are demonstrated by appropriate illustrative examples.
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