Reliability Evaluation And Optimization Of Phased-Mission Systems With Probabilistic Common Cause Failure

As the number of components included in an industrial system continues to increase,the complexity of its structure also continues to increase,resulting in a higher and higher probability of component failure.The failure of components may cause the system to fail,which will bring about economic losses that are difficult to estimate.In real life,most systems have multiple phases.Therefore,the reliability of phased-mission systems has been widely concerned by the society.However,most of the existing reliability evaluation methods for phased-mission systems have the problems of slow convergence speed and low calculation accuracy.Therefore,this paper takes the probabilitistic common cause failure phased-mission system as the research object,and conducts research on its reliability evaluation and optimization.First of all,this paper studies a phased-mission discrete-time Bayesian network method in view of the low calculation accuracy and slow convergence speed of the phased-mission system reliability evaluation method.This method first discretizes the running time of each phase into a fixed value,and gives the calculation expression of the width of the time segment after the running time of each phase is discretized.Secondly,based on the new dynamic gate conversion method,a dynamic phased-mission discrete-time Bayesian network is proposed,which improves the calculation accuracy of system reliability.Through comparative experimental analysis with the universal generating function method and Monte Carlo simulation method,it is verified that the phased-mission discrete-time Bayesian network method consumes less time than the Monte Carlo simulation method under the same calculation accuracy.At the same time,through the comparison experiment analysis with the universal generating function method and the traditional discrete-time Bayesian network method,it is verified that the proposed dynamic phased-mission discrete-time Bayesian network method has higher calculation accuracy than the traditional method.Secondly,for the phased-mission system of probabilistic common cause failure,this paper proposes an explicit probabilitistic common cause phased-mission discrete-time Bayesian network method.This method is based on the logic OR gate and directly incorporates the probabilitistic common cause failure into the system reliability assessment.The logic OR gate is connected to the two basic events of the independent failure of the component and the probabilistic common cause failure of the component.By evaluating the leaf node in a reliable state,we can obtains the reliability of the phased-mission mission system with probabilitistic common cause failure.Finally,a case is used to verify that the common cause failure causes the system reliability to decrease,and for the case,the influence of the probabilitisitc common cause failure on the system reliability is analyzed.Finally,this paper studies the reliability optimization method of the phased-mission system with probabilistic common cause failure.This method is based on the probabilitisitc importance method to get the importance ranking of each component at different phases,find the weak links of each phase and design redundant components to optimize the system structure,so as to achieve the goal of system reliability optimization.