针对发动机排气歧管在高低温载荷下易发生疲劳开裂的问题，及传统台架试验与用户实际使用脱节的难题，提出了基于损伤模型与大数据结合分析的排气歧管加速试验与可靠性评估方法。基于Coffin-Manson疲劳理论和Miner线性累积损伤法则，建立了以温度为驱动参数的排气歧管损伤量化模型，同步构建了排气歧管表面温度虚拟传感器模型。采集整车用户的运行数据，分析外推得到覆盖95%用户损伤水平的目标伪损伤值，并在发动机试验台架开展冷热冲击加速试验进行对比验证。研究结果表明，排气歧管表面温度虚拟传感器模型计算误差小于5%，满足工程应用需求。同时，通过台架试验及整车使用数据验证了损伤模型的准确性，预测优化后的排气歧管能满足整车用户超过60×10⁴ km的使用要求。损伤模型计算为发动机排气歧管的可靠性评估和寿命预测提供了依据。

To address the issue of fatigue cracking in engine exhaust manifolds under high and low-temperature loads and to bridge the gap between traditional bench tests and actual user operation, an accelerated test and reliability assessment method were proposed based on a combined analysis of damage modeling and big data. A temperature-driven quantitative damage model for the exhaust manifold was established based on the Coffin-Manson fatigue theory and Miners linear cumulative damage rule. Then a virtual sensor model for estimating the manifold surface temperature was developed. The operational data from vehicle users were collected and extrapolated to determine a target pseudo-damage value covering 95% of user damage levels. And the accelerated thermal shock tests were subsequently conducted on the engine test bench for comparative validation. The results demonstrate that the virtual sensor model achieves a calculation error of less than 5%, meeting engineering application requirements. The accuracy of damage model was verified through both bench tests and vehicle usage data, with predictions indicating that the optimized exhaust manifold could withstand over 600 000 kilometers of service under general user conditions. The proposed damage calculation model provides a reliable basis for the durability assessment and life prediction of engine exhaust manifolds.