The failure behaviour of modern systems is complex, with both dynamics and correlation. First, in order to describe the dynamic failure behaviour intuitively and accurately, a novel continuous-time dynamic Bayesian network analysis method is proposed, which uses node sequence conditional probability table(CPT) to describe the event relationship. Then, the calculation method of child node failure probability, posteriori probability and importance measures of root node based on the rule execution degree of node sequence CPT and the sampling property of impulse function is proposed. Further, aiming at the system correlation failure behaviour caused by common cause failure(CCF), a novel continuous-time dynamic Bayesian network analysis method considering CCF is proposed to solve the overlapping problem of system failure logic dynamics and correlation. Compared with the Bayesian network, discrete-time dynamic Bayesian network analysis method, Markov chain and Monte Carlo method, the feasibility and superiority of the proposed method are verified. Finally, the reliability of dynamic failure related systems is evaluated, the results show that the proposed method can directly and effectively describe the dynamic and correlation failure behavior, obtain the accurate system reliability index, compared with ignoring CCF, considering CCF can improve the reliability analysis accuracy of the system by 29% when the task time is 5×10 6 h, which is more practical.