Method for automatically extracting structural modal parameters by using clustering

A structural mode and automatic extraction technology, which is applied in the testing of machines/structural components, elastic testing, complex mathematical operations, etc., can solve problems such as difficult real-time identification and strong subjectivity of identification results, and achieve the effect of improving the degree of automation

Inactive Publication Date: 2018-08-10
DALIAN UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method for automatically extracting structural physical modal parameters to solve the problems of strong subjectivity of recognition results and difficulty in real-time recognition due to manual participation in recognition

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  • Method for automatically extracting structural modal parameters by using clustering
  • Method for automatically extracting structural modal parameters by using clustering
  • Method for automatically extracting structural modal parameters by using clustering

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Embodiment Construction

[0040] The embodiments of the present invention will be further explained below in combination with technical solutions and technical solutions.

[0041] Take an 8-ply shear frame, each layer has a mass of 1.10×10 6 kg, the stiffness of each layer is 1541.07×10 6 N / m, the damping adopts Rayleigh damping αM+βK, Rayleigh damping coefficient α=0.3000, β=0.0005, the excitation form is zero-mean Gaussian white noise excitation, the noise level is set to 20% of the random response variance, and the sampling frequency is 100Hz , the sampling signal is the acceleration of each layer of the frame.

[0042] The specific implementation is as follows:

[0043] (1) Process the structural acceleration response Y(t)=[y(t), y(t+1),...,y(t+N)] with the natural excitation method, and take the measuring point 1 as the reference point to obtain each Delay correlation function vector, r(τ)=[r 11 (τ)r 21 (τ)...r 81 (τ)] T , where r ij (τ) represents the cross-correlation function between th...

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Abstract

The invention belongs to the technical field of structural health monitoring, and proposes a method for automatically extracting structural modal parameters by using clustering. Firstly, the natural excitation technique is combined with the feature system to obtain the modal parameters of the structural random response under different calculation orders by the algorithm. Then, according to the feature that the physical modal is stable and the similarity is high and the false modal is instable and the similarity is low, the dissimilarity of the two most similar modals under the adjacent calculation orders is used as the feature of the model at a lower order, and the fuzzy C-means clustering is performed to adaptively acquire the stable modal class with high similarity. Finally, hierarchicalclustering is performed to classify the modals that appear in different calculation orders and have the same modal parameters into one class, and each physical modal of the structure can be obtainedin the same manner. The method obtains a stable modal by clustering modal dissimilarities rather than modal parameters themselves without human intervention.

Description

technical field [0001] The invention belongs to the field of structural health monitoring and relates to a method for automatically extracting engineering structure modal parameters. Background technique [0002] The change of structural modal parameters can reflect the health status of the engineering structure. In order to grasp the service performance of the structure, it is necessary to obtain the modal parameters of the structure in real time. In the existing research, the parametric mode recognition method has been widely used because of its clear physical parameter model, such as least squares complex frequency domain method, random subspace method and characteristic system realization algorithm. However, most of these methods require subjective experience when using them. Taking the characteristic system implementation algorithm as an example, the existence of environmental noise makes it difficult to determine the model order of the algorithm. If the order is too lo...

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Application Information

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IPC IPC(8): G06K9/62G06F17/50
CPCG06F30/20G06F18/23213G06F18/231G06F2218/12G01M5/00G06F17/16
Inventor 杨小梅伊廷华曲春绪李宏男
Owner DALIAN UNIV OF TECH
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