为了快速评估曲轴的可靠性，并且考虑发动机转速对曲轴疲劳寿命的影响，提出了一种基于动态贝叶斯网络的曲轴可靠度预测方法。首先通过对曲轴连杆机构进行多体动力学仿真，获得不同载荷下曲轴的受力时域数据。其次利用疲劳损伤累积理论计算得到曲轴在不同转速下的疲劳寿命。最后根据曲轴在不同转速下的运行时间和失效概率等参数，采用动态贝叶斯网络模型对曲轴的可靠性水平进行动态估计，确定曲轴可靠性随时间变化规律。结果表明，曲轴在第10年时可靠度为61.78%。

To rapidly assess the reliability of crankshaft and consider the impact of engine speed on the fatigue life of crankshaft, a prediction method for crankshaft reliability was proposed based on dynamic Bayesian networks. The multi-body dynamics simulations of the crankshaft connecting rod mechanism were first conducted to obtain time-domain force data under various loads. The fatigue lives of crankshaft at different speeds were calculated by utilizing fatigue damage accumulation theory. The dynamic Bayesian network model was then employed to dynamically estimate the level of crankshaft reliability based on parameters such as the operating time and failure probability of crankshaft at different speeds, therefore establishing the changing laws of crankshaft reliability over time. The results showed that the crankshaft reliability was 61.78% by the 10th year.