Monitoring signal filtering method based on wavelet analysis and threshold processing

Abstract

The invention discloses a monitoring signal filtering method based on wavelet analysis and threshold processing. The method comprises the following steps: obtaining signal data s, carrying out the real-time monitoring through arranging a sensor, obtaining the information monitoring data of the sensor, and enabling the obtained information monitoring data of the sensor to serve as the signal data s; selecting a wavelet function to carry out n-layer wavelet decomposition on the signal data s to respectively obtain an approximation coefficient and n layers of detail coefficients wjk; performing threshold processing on the detail coefficients from the nth layer to the nth layer by using a threshold method combining soft and hard threshold compromise and modular processing to obtain detail coefficients w'jk and approximation coefficients of each layer; and performing wavelet reconstruction on the obtained signal data s by using the detail coefficient w'jk and the approximation coefficient of each layer obtained after processing to obtain signal data s' after noise suppression. The method has the characteristic of high monitoring precision, and can accurately reflect the health condition of the bridge.

Description

technical field [0001] The invention relates to the technical field of monitoring signal filtering and processing of bridge structures, high-speed railway subgrades, tower tilts, side landslides, and debris flows, and specifically relates to a monitoring signal filtering method based on wavelet analysis and threshold value processing. Background technique [0002] With the rapid improvement of my country's economic strength, the number of bridges, high-speed railway subgrades, pole towers, and side landslides has increased year by year, and the proportion of large and medium-sized bridges has increased year by year. However, due to many factors such as structural degradation, natural factor damage, external force and environmental mutation, accidents such as bridges, high-speed railway subgrades, tower side landslide damage and even collapses are also increasing year by year. Therefore, it is necessary to monitor the health status of bridges, high-speed railway subgrades, po...

AI Technical Summary

Problems solved by technology

Therefore, it is necessary to monitor the health status of bridges, high-speed railway subgrades, pole towers, and side landslides. Currently, sensors are installed on bridges, high-speed railway subgrades, pole towers, and side landslides to monitor their own stress and deformation. Bridges, high-speed railways The health monitoring signal of roadbed and tower side landslides implies the health state information of the structure. When using sensors to monitor the health status of bridges, high-speed railway roadbeds, and tower side landslides, due to the impact of vehicle dynamic loads and surrounding environment vibrations, etc., it will cause The monitoring data jumps; the data monitored by the sensor cannot objectively reflect the health monitoring conditions of bridges, high-speed railway subgrades, and tower side landslides, and the effect of mudslide prevention monitoring is not good, and the monitoring error is large

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