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Higher-Order Spectra (HOS) for Identification of Nonlinear Modal Coupling

机译:高阶光谱(HOS),用于识别非线性模态耦合

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Over the past four decades considerable work has been done in the area of power spectrum estimation. The information contained within the power spectrum relates to a signal's autocorrelation or 'second-order statistics'. The power spectrum provides a complete statistical description of a gaussian process; however a problem with this information is that it is phase blind. This problem is addressed if one turns to a system's frequency response function (FRF). The FRF graphs the magnitude and phase of the frequency response of a system; in order to do this it requires information regarding the frequency content of the input and output signals. Situations arise in science and engineering whereby signal analysts are required to look beyond second-order statistics and analyse a signal's higher-order statistics (HOS). Higher-order statistics or spectra give information on a signal's deviation from gaussianity and consequently are a good indicator function for the presence of nonlinearity within a system. One of the main problems in nonlinear system identification is that of high modal density. Many modeling schemes involve making some expansion of the nonlinear restoring force in terms of polynomial or other basis terms. If more than one degree-of-freedom is involved this becomes a multivariate problem and the number of candidate terms in the expansion grows explosively with the order of nonlinearity and the number of degrees-of-freedom. This paper attempts to use HOS to detect and quantify nonlinear behaviour for a number of symmetrical and antisymmetrical systems over a range of degrees of freedom. In doing so the paper also attempts to show that HOS are a more sensitive tool than the FRF in detecting nonlinearity. Furthermore, the object of this paper is to try and identify which modes couple in a nonlinear manner in order to reduce the number of candidate coupling terms, for a model, as much as possible. The bispectrurn method has previously been applied to simple low-DOF systems with high symmetry and has been shown to work well in this limited case. The current paper will consider a model of a continuous Wing-Pylon model with reduced symmetry in order to assess the utility of the method in a more general situation, the analysis is extended to assess the utility of the trispectrum.
机译:在过去的四十年中,在功率谱估计领域已经完成了相当大的工作。在功率范围内包含的信息涉及信号的自相关或“二阶统计”。功率谱提供高斯过程的完整统计描述;然而,这些信息的问题是它是阶段盲。如果一个人转向系统的频率响应函数(FRF),则解决此问题。 FRF图表系统的频率响应的幅度和相位;为此,它需要关于输入和输出信号的频率内容的信息。科学和工程中出现的情况,其中信号分析师需要超越二阶统计,并分析信号的高阶统计(HOS)。高阶统计或光谱提供了关于信号与高斯的偏差的信息,因此是系统内非线性存在的良好指标功能。非线性系统识别中的主要问题之一是高模态密度。许多建模方案涉及在多项式或其他基础术语方面使非线性恢复力的一些扩展。如果涉及超过一种自由度,这成为多变量问题,并且扩张中的候选术语数量随着非线性的顺序和自由度的数量而大大增加。本文试图使用HOS检测和量化在一系列自由度的对称和防逆脉系统中的非线性行为。在这样做,本文还试图表明HOS是比检测非线性的FRF更敏感的工具。此外,本文的目的是尝试以非线性方式识别哪些模式,以便尽可能地减少模型的候选耦合术语的数量。先前已应用于具有高对称性的简单低DOF系统,并且已被证明在本有限壳体中运行良好。目前的纸张将考虑连续翼塔模型的模型,其对称性降低,以评估方法的效用在更一般的情况下,延长了分析以评估三谱的效用。

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