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

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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Problems solved by technology

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 nonlinear response is mostly located in the submanifold of the outer space, many nonlinear manifold learning algorithms have been proposed, but none of them have been applied in the field of modal parameter identification.

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