Wavelet Processing of Acoustic Emission Signal in Rock Using Pywavelet
|School||Wuhan University of Science and Technology|
|Keywords||Acoustic Emission Signal Process Wavelet Transform Denoise Pywavelet module|
With the development of underground mining in metal mine, pressure activity has been gradually revealed, what’s more, many small-scale mine methods are not standardized, which boost the formation of large gob and overload the pillar which gradually accumulate strain energy, then the pillar would split or even collapse, which would seriously endanger mine safety.Acoustic emission monitoring to pressure activity has been rapidly developed in recent decades and widely used in engineering and mining industry. Acoustic emission technology in mines need to address the position of monitoring points and the problem of signal denoising. This article possesses Puqing iron mine as the monitoring object which is in Daye city, Hubei province. In view of its exploitation, acoustic emission monitoring points were selected and the natural frequency experiment was consulted to acquire acoustic emission frequency of natural rock . To denoise the signal, Fast Fourier Transform and wavelet transform were compared. The main conclusions obtained by studying are as follows:1. As on-site condition constrains, acoustic emission monitoring points, which mainly distribut on the side of rock or pillar in the ongoing recovery level , are assigned by artificial arrangement. As mining continues, mined area has gradually become larger, some irregular pillars are over-pressured and splitting occurs, these pillars should be focused as monitoring objects.2. The rock acoustic emission frequency of Puqing iron mine is between 800KHz and 1000KHz, which would provide basis for using wavelet transform to denoise the frequency distribution of the signal, and take two different loading rates to demonstrate that under on-site condition, the more frequent pressure activity is, the more of the number of acoustic emission increase.3. Through the contrast of Fast Fourier Transform and wavelet transform, the advantage of wavelet transform on denoising has been worked out. With pywavelet module and matplotlib module in Python programming language, wavelet transform is used as denoising example, which means to remove the frequency which below the threshold and receive high signal to noise ratio through wavelet decompositon and reconstruction. The results are as good as expected.