Method for denoising acoustic testing data of porcelain insulator vibration based on wavelet decomposition threshold denoising

Abstract

The invention discloses a method for denoising acoustic testing data of porcelain insulator vibration based on wavelet decomposition threshold denoising. The method comprises the following steps: using a vibration acoustic method to detect a porcelain insulator to acquire a porcelain insulator vibration response signal containing noise; selecting a suitable wavelet basis function for the porcelain insulator vibration response signal containing noise and then carrying out multi-resolution wavelet decomposition, and transforming the vibration response from a time domain to a wavelet domain; rationally selecting a threshold function and a threshold, and machining a wavelet coefficient corresponding to the noise according to the threshold function; carrying out wavelet reconstruction, transforming the vibration response after denoising treatment to the time domain from the wavelet domain; storing the de-noised vibration response and sorting an acoustic testing result of the porcelain insulator vibration. According to the method, the noise in the acoustic testing data of the porcelain insulator vibration can be effectively filtered, so that sorting accuracy is improved.

Description

technical field [0001] The invention relates to a method for noise reduction of porcelain insulator vibration acoustic detection data based on wavelet decomposition threshold value denoising, which can eliminate the noise mixed in the data of porcelain insulator detection by acoustic vibration method, and avoid the influence of noise on classification results. Background technique [0002] When the vibroacoustic method is used to detect insulators, the stored test result data is a sound file in WAV format. Various factors in the test process will cause random noise signals to be mixed into the test data. The existence of these noise signals directly affects the test results. The accuracy and reliability of the classification affect the assessment of the mechanical performance status of porcelain insulators. Therefore, there is an urgent need for a method that can effectively filter the mixed random noise signal. [0003] In recent years, signal denoising processing based on...

AI Technical Summary

Problems solved by technology

Since the signal after noise interference may contain many spikes and mutations, the traditional Fourier analysis cannot give the change of the signal at a certain point in time, and any mutation of the signal on the time axis will affect the entire spectrum of the signal

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