Research on AE Signal Processing for Rock-like Materials Based on Wavelet Analysis
|Keywords||Acoustic Emission Wavelet Analysis Signal Processing LabVIEW|
Acoustic emission technology can be used to detect the movements of innerdefect of rock-like materials, and also to monitor and detect the whole dynamicprocess of the material’s damages. The received acoustic emission signals contain alarge amount of information of rock damages and all kinds of noise interference aswell. It is hard to interpret the information accurately by means of traditional signalprocessing methods. However, wavelet analysis provides an effective mathematicalway to extract the original signals or the characteristics of signals from the acousticemission signals with lots of noises, to raise the SNR (Signal to Noise Ratio) and thenrevise the error of the measuring system. This paper studies the processing method ofacoustic emission of rock-like materials.This paper summarizes domestic and overseas research situation of acousticemission recently, including several characteristics of acoustic emission such as thecharacteristic parameters, analyzing method and broadcasting model. Meanwhile, thecharacteristics of acoustic emission signals of rock-like materials were obtained bymeans of the analysis of single axle damaging process of rock, which reveals thedamaging mechanisms of rock-like materials.The properties of continuous wavelet and discrete wavelet, multiresolutionanalysis, and the discomposing and reconstructing process are also studied in thispaper.Aiming at the emergentness of acoustic emission signals of rock-like materials,meanwhile combined with Fourier Transform, simulating function of signal wasestablished to obtain the original signals and the signals with noises on a virtualdevelopment platform——LabVIEW8.6. Models were established by means ofLabVIEW8.6for comparative test of the choice of two wavelet basis——sym6anddb4, the confirmation of decomposition layer-number, and the confirmation of fourthreshold methods, which are respectively SURE Principle, Hybrid Principle,Universal Principle and Minimax Principle. Moreover, subVI of Noise RatioFormula, which was established on this platform, was used to attain the SNR in thetest to analyze the de-noising results.