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A method in model updating using Miscorrelation Index sensitivity

机译：使用不相关指数敏感性的模型更新方法

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摘要

This paper presents a new model updating method based on minimization of an index called Miscorrelation Index (MCI), which is introduced to localize the coordinates carrying error in a finite element (FE) model. MCI can be calculated from measured frequency response functions (FRFs) and dynamic stiffness matrix of the FE model for each coordinate as a function of frequency. Nonzero numerical values for MCI of a coordinate indicate errors in one or more elements of the system matrices corresponding to this coordinate. The sensitivity-driven model updating method presented in this study (MCI Sensitivity Method) is based on minimization of MCI. The application of the method is illustrated with four case studies. In the first and second examples a discrete system is considered, and computationally generated and polluted FRFs are used as pseudo-test data. In the third and fourth case studies, real test data is used and the performance of the method in practical applications is demonstrated on the benchmark structure built to simulate the dynamic behavior of an airplane, namely, GARTEUR SM-AG19 test bed. It is concluded that MCI Sensitivity Method yields successful results even when the measured responses of only a few coordinates are used, especially when miscorrelation is due to local errors.
机译：本文提出了一种新的基于最小化指标的模型更新方法，该指标称为不相关指数（MCI），该方法用于对有限元（FE）模型中带有误差的坐标进行定位。 MCI可以从测量的频率响应函数（FRF）和FE模型的动态刚度矩阵针对每个坐标作为频率的函数来计算。坐标的MCI的非零数值表示对应于该坐标的系统矩阵的一个或多个元素中的错误。本研究中提出的灵敏度驱动模型更新方法（MCI灵敏度方法）基于最小化MCI。通过四个案例研究说明了该方法的应用。在第一个和第二个示例中，考虑了一个离散系统，并将计算生成和污染的FRF用作伪测试数据。在第三个和第四个案例研究中，使用了真实的测试数据，并在用于模拟飞机动态行为的基准结构（即GARTEUR SM-AG19测试台）上验证了该方法在实际应用中的性能。结论是，即使仅使用几个坐标的测量响应，MCI灵敏度方法也能取得成功的结果，尤其是当由于局部误差而导致不相关时。

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