Abstract: Reliability analysis and fault diagnosis for dynamic systems have always been hot topics in this field. As one of the popular reliability analysis methods, dynamic bayesian network (DBN) has been fully studied. However, the existing DBN algorithm has no general inference engines, and the modeling difficulty increases exponentially with the system complexity. This paper proposes a general probability table modeling method, which can also be applied on the dynamic reliability analysis of the system under the continuous mission time. Additionally, via the Bayesian inference algorithm, the posterior probability of component failure can be obtained, which can also be applied on system fault diagnosis. Finally, the validation of proposed method is verified by the reliability analysis and fault diagnosis of the power system.