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- W2338901009 abstract "Fault-tree analysis is a useful analytic tool for the reliability and safety of complex system. However, fault tree is not suitable for repairable system. In this paper, we will propose a new method called TTF (time to failure) and TTM (time to maintenance) to analyze repairable system. Nevertheless, Monte Carlo simulation may be time consuming. In order to reduce simulation time, a parallel algorithm based on Spark will be used in this paper. Spark-MapReduce is the latest parallel computation framework. In situations where the amount of data is prohibitively large, we will propose a parallel algorithm for repairable system analysis to quickly get the simulation result. In this article, we propose a parallel algorithm to speed up through the experiment, we prove that the parallel algorithm has a superior performance on large scale models, and under Spark-MapReduce framework, researchers can concentrate on algorithm itself. It has significant benefits on reliability or availability assessment issues because it can free researchers, who are non-computer professional researchers, from parallelization and computational frame." @default.
- W2338901009 created "2016-06-24" @default.
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- W2338901009 date "2016-01-01" @default.
- W2338901009 modified "2023-09-22" @default.
- W2338901009 title "A spark-based parallel simulation approach for repairable system" @default.
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- W2338901009 doi "https://doi.org/10.1109/rams.2016.7447965" @default.
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