The profitability and feasibility of a system depends on the optimisation of the work of the subsystem as a unit. Here, the breakdown hours of an LHD machine is predicted by modelling a time series on a Neural Network. By accurately predicting the breakdown hours one can plan for efficient mining operations, maintenance and parts supply chain for replacements.
Shuffle Exchange Networks (SENs) are considered as an appropriate interconnection network because they consist of switching elements of small size and possess a straight forward and simple configuration. In this paper, we have proposed a method for analyzing reliability of 4×4 SEN, 4×4 SEN+1 and 4×4 SEN+2. The reliability has been obtained on the basis of three indices, namely, terminal reliability, broadcast reliability and network reliability by using universal generating function (UGF) method. This study also examines effect of adding the additional stages in 4×4 shuffle exchange networks (SENs).
A multi-state weighted K-out-of-n system is of great importance in system engineering due to its generalized nature. In earlier studies, usually all the components of the multi-state weighted K-out-of-n system were considered to be in a steady state though we can encounter engineering systems where the components of this type of system can be dynamic in nature, that is, the states of components change over time. To deal with this situation, in the current study an algorithm, based on 𝐿 𝑧 -transform is presented to determine the reliability indices of multi-state weighted K-out-of-n system in dynamic setup. The components of the system are assumed to follow Markov process and are repaired only when they are completely failed. In this proposed study reliability, availability, instantaneous mean expected performance, and performance deficiency are obtained for the considered system. Finally, a case study of the power system of warship is considered to apply and validate the proposed approach.
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