Statistical model based bridge health monitoring data wavelet denoising method

A technology for bridge health monitoring and wavelet noise reduction, applied in electrical digital data processing, special data processing applications, calculations, etc., can solve problems such as poor reliability and noise

Inactive Publication Date: 2015-09-02
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0010] In order to overcome the shortcomings of existing bridge health monitoring data such as noise and poor reliability, the present invention provides a wav

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  • Statistical model based bridge health monitoring data wavelet denoising method
  • Statistical model based bridge health monitoring data wavelet denoising method
  • Statistical model based bridge health monitoring data wavelet denoising method

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Embodiment Construction

[0056] The present invention will be further described below in conjunction with the accompanying drawings.

[0057] refer to Figure 1 to Figure 9 , a method for wavelet denoising of bridge health monitoring data based on a statistical model, said method comprising the steps of:

[0058] Step 1) Establishment of bridge monitoring signal model

[0059] It is generally believed that the envelope signal collected by the bridge structure health monitoring system consists of two parts, one is the real and meaningful bridge structure response signal, and the other is the noise signal. One of the causes of noise is caused by the thermal, magnetic and electric effects of signal acquisition instruments and signal transmission equipment, and the other is caused by observation errors.

[0060] A model of a noisy monitoring signal can be expressed as follows:

[0061] Q(t)=f(t)+δ(t) (t=0,1...n-1) (1)

[0062] In the formula, f(t) is the real signal, δ(t) is the noise, Q(t) is the sig...

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Abstract

The invention relates to a statistical model based bridge health monitoring data wavelet denoising method. The method includes the following steps: firstly, establishing a bridge monitoring signal model; secondly, subjecting an obtained structure monitoring signal to wavelet decomposition to obtain two frequency domains, namely a low frequency domain A1 and a high frequency domain D1, continuing performing wavelet decomposition on the low frequency domain A1 to obtain two frequency domains, namely a low frequency domain A2 and a high frequency domain D2, and repeating the step till maximum layers are decomposed; thirdly, subjecting the actual monitoring signal to wavelet decomposition and establishing a statistical model of wavelet decomposition coefficients; fourthly, deducing a wavelet threshold contracting function and subjecting the wavelet coefficients of a high-frequency part (Dj,j=1,2,...J) of each layer to thresholding method contracting processing; fifthly, performing wavelet inverse transformation processing to obtain denoised bridge structure monitoring data. Denoising is performed effectively, quality of the monitoring data is improved, and signal smoothness is improved.

Description

technical field [0001] The invention is applied to the field of bridge health monitoring data denoising, and relates to a statistical model-based wavelet denoising method suitable for bridge structure monitoring data. Background technique [0002] my country's "Measures for the Safety Operation and Management of Highway Long Bridges and Tunnels (Draft for Comment)" proposes that the safety operation management of national highways and provincial highway super-large bridges should implement the work policy of "safety first, prevention first", and recommends that the management and maintenance units adopt modern information technology , gradually establish a safety monitoring system for long bridges and tunnels, establish and improve the safety monitoring and evaluation system for long bridges and tunnels, conduct real-time monitoring of the working environment, structural status, and response of bridges and tunnels under various external loads, and timely grasp the long-term Th...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 余佩琼杨立陈鹏吴远吕常新赵玉贤
Owner ZHEJIANG UNIV OF TECH
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