Wang Dengjiang, He Jingjing, Guan Xuefei, Yang Jinsong, Zhang Weifang
School of Reliability and Systems Engineering, Beihang University, 37 Xueyuan Rd., Haidian Dist., Beijing 100191, China.
School of Reliability and Systems Engineering, Beihang University, 37 Xueyuan Rd., Haidian Dist., Beijing 100191, China.
Ultrasonics. 2018 Mar;84:319-328. doi: 10.1016/j.ultras.2017.11.017. Epub 2017 Dec 2.
This paper presents a study on model assessment for predicting structural fatigue life using Lamb waves. Lamb wave coupon testing is performed for model development. Three damage sensitive features, namely normalized energy, phase change, and correlation coefficient are extracted from Lamb wave data and are used to quantify the crack size. Four data-driven models are proposed. The average relative error and the probability of detection (POD) are proposed as two measures to evaluate the performance of the four models. To study the influence of model choice on the probabilistic fatigue life prediction, probability density functions of the actual crack size are obtained from the POD models given the Lamb wave data. Crack growth model parameters are statistically identified using Bayesian parameter estimation with Markov chain Monte Carlo simulations. The model assessment and the influence of model choice on fatigue life prediction are made using both coupon testing data with artificial cracks and realistic lap joint testing data with naturally developed cracks.
本文介绍了一项关于使用兰姆波预测结构疲劳寿命的模型评估研究。为了模型开发进行了兰姆波试样测试。从兰姆波数据中提取了三个损伤敏感特征，即归一化能量、相位变化和相关系数，并用于量化裂纹尺寸。提出了四个数据驱动模型。提出了平均相对误差和检测概率（POD）作为评估这四个模型性能的两项指标。为了研究模型选择对概率疲劳寿命预测的影响，在给定兰姆波数据的情况下，从POD模型中获得实际裂纹尺寸的概率密度函数。使用马尔可夫链蒙特卡罗模拟的贝叶斯参数估计对裂纹扩展模型参数进行统计识别。使用带有人工裂纹的试样测试数据和带有自然形成裂纹的实际搭接接头测试数据进行模型评估以及模型选择对疲劳寿命预测的影响研究。
