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HIGH-SENSITIVITY MASS SENSING BASED ON ENHANCED NONLINEAR DYNAMICS AND ATTRACTOR MORPHING MODES

机译:基于增强的非线性动力学和吸引子变形模式的高灵敏度质量感测

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摘要

This paper demonstrates two novel methods for identifying small parametric variations in an experimental system based on the analysis of sensitivity vector fields (SVFs) and probability density functions (PDFs). The experimental system includes a smart sensing beam excited by a nonlinear feedback excitation through two PZT (lead zirconate titanate) patches symmetrically bonded on both sides at the root of the beam. The nonlinear feedback excitation requires the measurement of the dynamics (e.g. velocity of one point at the tip of the beam) and a nonlinear feedback loop, and is designed such that the beam vibrates in a chaotic regime. Changes in the state space attractor of the dynamics due to small parametric variations can be captured by SVFs which, in turn, are collected by applying point cloud averaging (PCA) to points distributed in the attractors for nominal and changed parameters. Also, the PDFs characterize statistically the distribution of points in the attractors. The differences between the PDFs of the attractors for different changed parameters and the baseline attractor can provide different attractor morphing modes for identifying variations in distinct parameters. The experimental results based on the proposed approaches show that very small amounts of added mass at different locations along the beam can be accurately identified.
机译:本文演示了两种基于灵敏度矢量场(SVF)和概率密度函数(PDF)的分析来识别实验系统中小参数变化的新颖方法。该实验系统包括一个智能感应光束,该光束通过非线性反馈激发,通过两个对称地束缚在光束根部两侧的PZT(钛酸锆钛酸铅)斑片进行激发。非线性反馈激励需要对动力学进行测量(例如,梁尖端的一个点的速度)和非线性反馈环路,并且需要进行设计以使梁在混沌状态下振动。 SVF可以捕获由于小参数变化而引起的动力学状态空间吸引子的变化,而SVF可以通过将点云平均(PCA)应用于吸引子中分布的标称和更改参数的点来收集。而且,PDF可以统计地描述吸引子中点的分布。对于不同的已更改参数,吸引器的PDF与基准吸引器之间的差异可以提供不同的吸引器变形模式,以识别不同参数的变化。基于所提出的方法的实验结果表明,可以精确地识别沿梁的不同位置处的添加质量非常小。

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