Statistical learning prediction of fatigue crack growth via path slicing and re-weighting

作     者：Yingjie Zhao Yong Liu Zhiping Xu Yingjie Zhao;Yong Liu;Zhiping Xu

作者机构：Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing100084China 

出 版 物：《Theoretical & Applied Mechanics Letters》 (力学快报（英文版）)

年 卷 期：2023年第13卷第6期

页      面：415-423页

核心收录：

学科分类：08[工学] 0801[工学-力学（可授工学、理学学位）] 080102[工学-固体力学] 

基　　金：the National Natural Science Foundation of China(Grant Nos.52090032 and 11825203) 

主　　题：Fatigue crack growth Structural health monitoring Statistical noises Rare events Digital libraries 

摘      要：Predicting potential risks associated with the fatigue of key structural components is crucial in engineering ***,fatigue often involves entangled complexities of material microstructures and service conditions,making diagnosis and prognosis of fatigue damage *** report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with *** libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical *** reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack ***-slicing and re-weighting techniques are introduced to handle the statistical noises and rare *** predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack *** end-to-end approach is validated by representative examples with fatigue cracks in plates,which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.