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Optimal Selection of Artificial Boundary Conditions for Model Update and Damage Detection - Part 2: Experiment

机译：用于模型更新和损伤检测的人工边界条件的最佳选择 - 第2部分：实验

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摘要

Sensitivity-based model error localization and damage detection is hindered by the relative differences in modal sensitivity magnitude among updating parameters. The method of artificial boundary conditions is shown to directly address this limitation, resulting in the increase of the number of updating parameters at which errors can be accurately localized. Using a single set of FRF data collected from a modal test, the artificial boundary conditions (ABC) method identifies experimentally the natural frequencies of a structure under test for a variety of different boundary conditions, without having to physically apply the boundary conditions, hence the term "artificial." The parameter-specific optimal ABC sets applied to the finite element model will produce increased sensitivities in the updating parameter, yielding accurate error localization and damage detection solutions. A method is developed for identifying the parameter-specific optimal ABC sets for updating or damage detection, and is based on the QR decomposition with column pivoting. Frequency response data collected from a simple laboratory experiment is used, along with the corresponding finite element-generated data, to demonstrate the effectiveness of the ABC-QR method.
机译：基于灵敏度的模型误差定位和损坏检测受更新参数中模态灵敏度幅度的相对差异的阻碍。示出了人工边界条件的方法直接解决了这种限制，导致更新的更新参数的数量增加，在该误差可以准确地定位。使用从模态测试中收集的单组FRF数据，人工边界条件（ABC）方法通过实验识别出在各种不同边界条件下测试的结构的自然频率，而无需物理地应用边界条件，因此术语“人为”。应用于有限元模型的参数特定的最佳ABC集将在更新参数中产生增加的灵敏度，产生准确的误差定位和损坏检测解决方案。开发了一种用于识别用于更新或损坏检测的参数特定最佳ABC集的方法，并且基于用列枢转的QR分解。从简单的实验室实验中收集的频率响应数据以及相应的有限元生成的数据，以证明ABC-QR方法的有效性。

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