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A Method for Identifying Abnormal Conditions of Bridge Monitoring Data Based on Multiple Wavelets

A technology for monitoring data and abnormal conditions, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as unsatisfactory field application conditions

Active Publication Date: 2020-04-28
重庆物康科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

There are relatively few research results in this field, and the field application is not satisfactory

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  • A Method for Identifying Abnormal Conditions of Bridge Monitoring Data Based on Multiple Wavelets
  • A Method for Identifying Abnormal Conditions of Bridge Monitoring Data Based on Multiple Wavelets
  • A Method for Identifying Abnormal Conditions of Bridge Monitoring Data Based on Multiple Wavelets

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Embodiment Construction

[0012] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings; it should be understood that the preferred embodiments are only for illustrating the present invention, rather than limiting the protection scope of the present invention.

[0013] Such as figure 1 As shown, a method for identifying abnormalities in bridge monitoring data based on multiple wavelets includes the following steps:

[0014] 1) Intercept a certain type of bridge monitoring data (such as crack monitoring data, deflection monitoring data, strain monitoring data, tilt monitoring data, etc.) at a certain measuring point within a period of time as the original signal S to be analyzed.

[0015] 2) The first stage of wavelet processing (extraction of original signal trend): use Daubechies wavelet for multi-scale decomposition of the original signal, as shown in the attached figure 1 . The original signal S is decomposed into a low-fr...

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Abstract

The invention discloses a method for identifying abnormal situations of bridge monitoring data based on multiple wavelets. Set the signal SAn as the signal P, decompose P by wavelet at m levels, and obtain the decomposed first-level signal PD1; 4): take the absolute value of PD1 as the characteristic signal Q, decompose Q by wavelet at j levels, and obtain the decomposed The low-frequency sub-band signal QAj of the jth layer; the absolute value of QAj represents the degree of variation of the original signal S; 5) When |QAj|>k at time t, it means that the original signal S is abnormal at time t. The present invention analyzes the mass data of bridge monitoring and adopts multiple wavelet technology to extract useful signals reflecting the operating state of the bridge from the original monitoring signals containing noise, and determines whether the operating state of the bridge has changed by judging whether the useful signal exceeds the limit, so as to achieve The purpose of identifying anomalies in bridge monitoring data.

Description

technical field [0001] The invention relates to the field of analysis and processing of bridge monitoring data, in particular to a multi-wavelet-based identification method for abnormal situations of bridge monitoring data. Background technique [0002] In recent years, with the rapid development of the national economy and the construction of the national transportation network, the total number of highway bridges has continued to increase. According to the "Statistical Bulletin on the Development of the Transportation Industry in 2015", by the end of 2015, there were 779,200 highway bridges nationwide, 3,894 super-large bridges, and 79,512 large-scale bridges. However, bridges that have been completed and put into operation must be affected by factors such as environment, load, and aging during their service, which will lead to structural performance degradation and pose safety hazards; in addition, the ever-increasing traffic volume has also made many bridges The design ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00
CPCG06F2218/08
Inventor 唐浩孟利波廖敬波宋刚段敏陈果李志刚
Owner 重庆物康科技有限公司