FPGA-based Monte Carlo simulation for fault tree analysis

Abstract The reliability analysis of critical systems is often performed using fault-tree analysis. Fault trees are analyzed using analytic approaches or Monte Carlo simulation. The usage of the analytic approaches is limited in few models and certain kinds of distributions. In contrast to the analytic approaches, Monte Carlo simulation can be broadly used. However, Monte Carlo simulation is time-consuming because of the intensive computations. This is because an extremely large number of simulated samples may be needed to estimate the reliability parameters at a high level of confidence. In this paper, a tree model, called Time-to-Failure tree, has been presented, which can be used to accelerate the Monte Carlo simulation of fault trees. The time-to-failure tree of a system shows the relationship between the time to failure of the system and the times to failures of its components. Static and dynamic fault trees can be easily transformed into time-to-failure trees. Each time-to-failure tree can be implemented as a pipelined digital circuit, which can be synthesized to a field programmable gate array (FPGA). In this way, Monte Carlo simulation can be significantly accelerated. The performance analysis of the method shows that the speed-up grows with the size of the fault trees. Experimental results for some benchmark fault trees show that this method can be about 471 times faster than software-based Monte Carlo simulation.