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Real time detection of stiffness change using a radial basis function augmented observer formulation

机译:使用径向基函数增强的观察者公式实时检测刚度变化

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摘要

Existing methods for structural health monitoring pose a formidable challenge to real time implementation due to the significantly large computational loads. The proposed algorithm is suitable for online applications because it maintains good pattern recognition capabilities while possessing a computationally compact network topology. This study employs the computational efficiency of single layer radial basis function (RBF) approximaters to create a subspace capable of isolating faults in multi-degree of freedom systems which involve coupled and uncoupled stiffness changes in real time. The RBF network transforms the displacement-time history of the varying plant into a decoupled output space which is then compared to a baseline healthy observer which undergoes the same decoupling transformation. The online comparison of the output of the time varying plant and the healthy observer in a decoupled subspace comprises the observer based error function. The error function is shown to not only detect the existence of faults, but also isolate these faults in real time in the presence of base excitation. The method is validated for systems that experience earthquake induced damage, as well as an experimental system using a semi-active independent variable stiffness device which is capable of varying system stiffness in real time. By simply observing the displacement-time history responses, the RBF augmented observer formulation is capable identifying changes in the stiffness at each degree of freedom.
机译:由于极大的计算量,现有的结构健康监测方法对实时实施提出了巨大的挑战。所提出的算法适用于在线应用,因为它拥有良好的模式识别能力,同时拥有计算紧凑的网络拓扑。这项研究利用单层径向基函数(RBF)逼近器的计算效率来创建一个子空间,该子空间能够实时隔离涉及耦合和非耦合刚度变化的多自由度系统中的故障。 RBF网络将变化的植物的位移时间历史记录转换为解耦的输出空间,然后将其与经过相同解耦转换的基线健康观察者进行比较。在解耦子空间中时变植物和健康观察者的输出的在线比较包括基于观察者的误差函数。显示的误差函数不仅可以检测故障的存在,而且可以在存在基本激励的情况下实时隔离这些故障。该方法已针对遭受地震引起的破坏的系统以及使用半主动独立变刚度装置的实验系统进行了验证,该装置可以实时改变系统刚度。通过简单地观察位移-时间历史响应,RBF增强的观察者公式能够识别每个自由度下的刚度变化。

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