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Statistical updating of finite element model with Lamb wave sensing data for damage detection problems

机译：用兰姆波感测数据对损伤检测问题进行有限元模型的统计更新

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Health monitoring of large structures with embedded, distributed sensor systems is gaining importance. This study proposes a new probabilistic model updating method in order to improve the damage prediction capability of a finite element analysis (FEA) model with experimental observations from a Lamb-wave sensing system. The approach statistically calibrates unknown parameters of the FEA model and estimates a bias-correcting function to achieve a good match between the model predictions and sensor observations. An experimental validation study is presented in which a set of controlled damages are generated on a composite panel. Time-series signals are collected with the damage condition using a Lamb-wave sensing system and a one dimensional FEA model of the panel is constructed to quantify the damages. The damage indices from both the experiments and the computational model are used to calibrate assumed parameters of the FEA model and to estimate a bias-correction function. The updated model is used to predict the size (extent) and location of damage. It is shown that the proposed model updating approach achieves a prediction accuracy that is superior to a purely statistical approach or a deterministic model calibration approach.
机译：具有嵌入式，分布式传感器系统的大型结构的健康监控变得越来越重要。这项研究提出了一种新的概率模型更新方法，以利用Lamb-wave传感系统的实验观测结果来提高有限元分析（FEA）模型的损伤预测能力。该方法对FEA模型的未知参数进行统计校准，并估计偏差校正功能，以在模型预测和传感器观测值之间实现良好匹配。提出了一项实验验证研究，其中在复合面板上产生了一组受控损害。使用兰姆波感测系统收集具有损坏条件的时间序列信号，并构建面板的一维FEA模型以量化损坏。来自实验和计算模型的损伤指数均用于校准FEA模型的假定参数并估计偏差校正函数。更新的模型用于预测损坏的大小（范围）和位置。结果表明，所提出的模型更新方法可实现优于纯统计方法或确定性模型校准方法的预测精度。

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