Numerical Simulation to Reliability Analysis of Fault-Tolerant Repairable System

来源 :Journal of Shanghai Jiaotong University(Science) | 被引量 : 0次 | 上传用户:supxch
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In the traditional method for the reliability analysis of fault-tolerant system,the system structure is described by means of binary decision diagram (BDD) and Markov process,and then the reliability indexes are calculated.However,as the size of system augments,the size of state space will increase exponentially.Additionally,Markov approach requires that the failure and repair time of the components obey an exponential distribution.In this study,by combining dynamic fault tree (DFT) and numerical simulation based on the minimal sequence cut set (MSCS),a new method to evaluate reliability of fault-tolerant system with repairable components is proposed.The method presented does not depend on Markov model,so that it can effectively solve the problem of the state-space combination explosion.Moreover,it is suitable for systems whose failure and repair time obey an arbitrary distribution.Therefore,our method is more flexible than the traditional method.At last,an example is given to verify the method. In the traditional method for the reliability analysis of fault-tolerant system, the system structure is described by means of binary decision diagram (BDD) and Markov process, and then the reliability indexes are calculated. However, as the size of system augments, the size of state space will increase exponentially. Additionally, Markov approach requires that the failure and repair time of the components obey an exponential distribution. In this study, by combining dynamic fault tree (DFT) and numerical simulation based on the minimal sequence cut set ( MSCS), a new method to evaluate reliability of fault-tolerant system with repairable components is proposed. The method presented does not depend on Markov model, so that it can can solve the problem of the state-space combination explosion. Moreover, it is suitable for systems whose failure and repair time obey an arbitrary distribution. Beforefore, our method is more flexible than the traditional method. At last, an example is given to verify t he method.
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