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Combined non-parametric and parametric approach for identification of time-variant systems

机译:结合非参数和参数方法识别时变系统

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摘要

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.
机译:识别具有可变物理参数的系统,结构和机器是一项艰巨的任务,尤其是在涉及时变振动模式时。本文提出了一种新的组合的两步法-非参数和参数-建模方法,以便根据输入输出测量确定随时间变化的振动模式。第一步,使用基于时频小波的滤波器,从多自由度(MDOF)非参数系统表示中提取单自由度(SDOF)振动模式。第二步涉及使用具有外生输入的递归线性自回归移动平均值(ARMAX)模型,对提取的模式进行时变参数表示。使用基于随机变质的实验质量变化的MDOF框架状结构的系统识别分析,证明了该组合方法。结果表明,所提出的组合方法使用模型上的最少先验信息正确地捕获了所分析结构的动力学。

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