摘要
文中基于集成数据驱动方法 (临界合成少数过采样技术、极端梯度提升算法、深度卷积神经网络)建立了一种新型疲劳寿命智能预测模型.其中,临界合成少数过采样技术用于疲劳性能数据库的数据增强,极端梯度提升算法实现疲劳寿命影响因素的权重分析,深度卷积神经网络作为模型框架用于理解疲劳寿命及其影响因素之间的多重非线性关系.根据不同技术组合的分析,发现权重分析和数据增强均有利于预测精度的提高,前者效果优于后者.并通过与其他新颖预测模型的对比,验证了所提出的方法的预测准确度和稳定性.
In this study,a novel intelligent fatigue life prediction approach was established via the integrated data-driven method(Borderline-Synthetic Minority Over-Sampling Technique,eXtreme Gradient Boosting,Deep Convolutional Neural Network).Among them,the Borderline-Synthetic Minority Over-Sampling Technique was used to enhance the data quality of the fatigue performance dataset,the eXtreme Gradient Boosting was used to realize the weight analysis of the influencing factors of fatigue life,and the Deep Convolutional Neural Network was used as the model framework to understand the multiple nonlinear relationships between fatigue life and its influencing factors.Based on the analysis of different technology combinations,it was found that weight analysis and data augmentation were both beneficial for improving prediction accuracy,with the former having better results than the latter.And by comparing with other novel prediction models,the accuracy and stability of the proposed method were verified.Highlights:(1)A novel data-driven solution for fatigue life prediction of welded joints is provided.(2)The importance of different data processing modules for reliable fatigue life prediction is emphasized.(3)The influence of different model parameters on the performance of data-driven model is investigated,and the optimal parameters are obtained.
作者
冯超
赵雷
徐连勇
韩永典
FENG Chao;ZHAO Lei;XU Lianyong;HAN Yongdian(School of Materials Science and Engineering,Tianjin University,Tianjin 300350,China;Tianjin Key Laboratory of Advanced Joining Technology,Tianjin 300350,China)
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2023年第11期8-13,51,I0003,共8页
Transactions of The China Welding Institution
基金
国家杰出青年科学基金项目经费资助(52025052)。
