中国机械工程2026，Vol.37Issue(5)：1017-1025,9.DOI:10.3969/j.issn.1004-132X.2026.05.001
基于物理信息神经网络的风电机组塔筒概率疲劳寿命预测方法
A Probabilistic Fatigue Life Prediction Method for Wind Turbine Towers Based on a Physics-informed Neural Network
摘要
Abstract
To address the limitations that the traditional fatigue design for wind turbine towers using deterministic S-N curves might not accurately quantify fatigue life dispersion,a probabilistic fatigue life pre-diction method was proposed based on physics-informed neural networks.By embedding the physical prior knowledge such as fatigue life dispersion,monotonicity and nonlinearity into the neural networks,a proba-bilistic prediction model capable of accurately quantifying uncertainty was constructed.Compared with tra-ditional methods,the proposed method reduces the normalized root mean square error(NRMSE)by up to 31.58%.A 16 MW wind turbine simulation model was established in accordance with IEC standards,and tower load data were obtained by using Bladed software.Combined with wind-speed distribution,rain flow counting and the Miner rule,the probabilistic fatigue life prediction of the towers was achieved.The results show that the proposed method effectively characterizes the probabilistic features of fatigue damages,and the tower lifetime varies significantly with reliability requirements(shortening from 83.3 years at 50%probability to 18.2 years at 99.9%probability),which provides a reliable basis for probabilistic fatigue de-sign and safety assessment of wind turbine towers.关键词
风电机组/塔筒/概率疲劳寿命/物理信息神经网络/不确定性量化Key words
wind turbine/tower/probabilistic fatigue life/physics-informed neural network/uncer-tainty quantification分类
能源科技引用本文复制引用
谢冰冰,赵峰,郭昕兴,乔莉,程思创,刘晓辉,张桐舟,胡伟飞..基于物理信息神经网络的风电机组塔筒概率疲劳寿命预测方法[J].中国机械工程,2026,37(5):1017-1025,9.基金项目
国家自然科学基金(52275275) （52275275）
浙江省"尖兵""领雁"研发攻关计划(2023C01008) （2023C01008）
中车集团重大项目(2024CYY023,2025CXA290) （2024CYY023,2025CXA290）
