Manifold learning and Hilbert-Huang transformation combined structural modal parameter identification method

A manifold learning and structural modality technology, applied in special data processing applications, electrical digital data processing, instruments, etc., can solve problems such as the inability to discover internal sub-manifold structures, and the application of unexpanded modal parameter identification fields

Pending Publication Date: 2018-10-02
XI AN JIAOTONG UNIV
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However, these algorithms are linear dimensionality reduction methods, which can only discover the global Euclidean distance of the structure but cannot discover the intrinsic submanifold structure.
However, since the no

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  • Manifold learning and Hilbert-Huang transformation combined structural modal parameter identification method
  • Manifold learning and Hilbert-Huang transformation combined structural modal parameter identification method
  • Manifold learning and Hilbert-Huang transformation combined structural modal parameter identification method

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[0055] The technical scheme of the present invention will be further described below in conjunction with the accompanying drawings.

[0056] The invention is a structural mode parameter identification method based on the combination of manifold learning and Hilbert-Huang transformation. When the LLE algorithm is used for mode parameter identification, the response data is regarded as a high-dimensional data set. From the perspective of geometric feature extraction, mode shapes are considered as inherent properties of high-dimensional datasets. Using the LLE algorithm to reduce the dimensionality of the high-dimensional response data set, the mode shape and natural frequency can be obtained.

[0057]The data distributed on the high-dimensional manifold can be approximately regarded as distributed on a low-dimensional hyperplane in a small local area. In this neighborhood, it can be assumed that there is a linear mapping between the high-dimensional data and the low-dimensional ...

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Abstract

The invention discloses a manifold learning and Hilbert-Huang transformation combined structural modal parameter identification method. The method comprises the following steps of: 1, acquiring time domain response data of a measure point in a structure; 2, processing the time domain response data acquired in the step 1 by adoption of a manifold learning algorithm so as to obtain a vibration modeand a fixed frequency of the structure; and 3, processing the time domain response data acquired in the step 1 by adoption of a Hilbert-Huang transformation method so as to obtain a damping ratio of the structure. Compared with the prior art, the method has the beneficial effects as follows: 1, when modal parameter extraction is carried out by utilizing the manifold learning and Hilbert-Huang transformation combined method, vibration modes, fixed frequencies and damping ratios with relatively high precision can be obtained through response data when material parameters and experiment conditions of structures are unknown; and 2, the method can be used for processing nonlinear data and retaining nonlinear manifolds of the structures.

Description

technical field [0001] The invention belongs to the technical field of structural dynamic parameter identification, and in particular relates to a structural mode parameter identification method. Background technique [0002] The key to structural modal analysis is the identification of modal parameters, including modal frequencies, modal shapes, and damping ratios. These parameters are generally obtained through modal tests. However, due to the complex environment and technical limitations, it is often difficult to implement effective modal excitation and excitation force measurement, resulting in difficulties in obtaining the modal parameters of complex or large-scale structures. In comparison, response data are easier to obtain from experiments. In order to overcome this problem, many methods for modal parameter identification based only on response data have been proposed in the prior art. The traditional signal processing method is mainly based on Fourier transform, w...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/23G06F2119/06
Inventor 董龙雷郝彩凤张静静赵建平刘振骆保民官威
Owner XI AN JIAOTONG UNIV
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