In order to obtain the dynamic change law of reliability of redundant repairable system under the influence of common cause failure, a new algorithm combining improved GO method with dynamic bayesian network was proposed. Firstly, the GO diagram model was established according to the schematic diagram and flow chart of the system. Then, the GO model was converted into a DBN by using the conversion mapping rules of each operator and signal flow to the DBN. Finally, the model was solved by using DBN mature software and algorithms. After verification and comparison, it can be seen that this method is not only able to obtain the dynamic curve of system reliability and availability changing with time, but also to treat the influence of maintenance factors and common cause failures on the analysis results. In additional, that this method with the powerful ability of reverse reasoning, is able to obtain the weak links quickly and provide reference the fault diagnosis for the system

In order to solve the dynamic failure problem of pulverized coal pressurized conveying system, a reliability evaluation method based on dynamic bayesian network is proposed in this paper.Firstly, based on the analysis of the structure and function of the pulverized coal pressurized conveying system, the fault tree model is constructed, and the reliability analysis model of the pulverized coal pressurized conveying system is established according to the rules of the trans⁃formation from the fault tree model to the dynamic bayesian network.Then, by introducing maintenance factors and time series, using the two⁃way reasoning ability of dynamic bayesian network, the weak links and the dynamic variation law of reliability of the pulverized coal conveying system are determined.The results show that the method can effectively describe the dynamic characteristics of the pulverized coal pressurized conveying system, and the analysis results are more in line with the actual working conditions.