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Enhanced data-driven Damage Detection for Structural Health Monitoring Systems

机译:用于结构健康监测系统的增强型数据驱动型损伤检测

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In structural engineering, it is essential to monitor the operation condition of an aging structure. Thus, damage detection is widely used for structure monitoring. The aim of this work is to propose an adaptive kernel PLS based GLRT chart to improve the detection of damage in civil structural systems. The proposed technique aims to integrate the advantages of the adaptive nonlinear input-output model (kernel PLS) with those of GLRT chart. This technique will be tested using a simulated benchmark structure through the surveillance model variables. The technique based on adaptive representation is found to be more effective over the conventional technique.
机译:在结构工程中,监视老化结构的运行状况至关重要。因此,损伤检测被广泛用于结构监视。这项工作的目的是提出一种基于自适应核PLS的GLRT图,以改善对民用建筑系统中损坏的检测。所提出的技术旨在将自适应非线性输入输出模型(内核PLS)的优点与GLRT图的优点相结合。将通过监视模型变量使用模拟的基准结构对该技术进行测试。发现基于自适应表示的技术比常规技术更有效。

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