Signal processing of acoustic emission in coal or rock failure based on wavelet transform
The creation of acoustic emission (AE) signal in local parts of rock materials is the result of elastic energy releasing caused by quick load-off of rock. AE technology can be used to judge the stability of rock or coal. Using AE technology to predict dynamical catastrophe, like the gas outburst and bump, is the most promising method universally acknowledged by many researchers. However, there are a lot of noi ses in the AE signal collected in engineering practice that greatly affect the automatic identification of sig nal and the reliability of catastrophe prediction. In order to solve the problem, wavelet time-frequency analysis and denoising of AE signal monitored in the process of tunneling the laneway were carried out ac cording to the characteristic of coal AE. The results indicate that the wavelet base db6 in Daubechies wavelet family is an effective wavelet base for coal AE signal processing. Wavelet time-frequency analysis can identify effectively the noise of 300 Hz and effective signal in AE data. The frequency of effective AE signal gradually increases from 1000Hz to 2000Hz, and the bandwidth gradually increases too. Wavelet a nalysis can denoise the signal effectively, and can improve signal-to-noise ratio of data.
rock failure acoustic emission wavelet transform denoise time-frequency analysis
Huiming Yang GuangcaiWen YinhuiZou
Chongqing Institute of China Coal Research Institute, Chongqing 400037,China
国际会议
重庆
英文
108-114
2009-05-20（万方平台首次上网日期，不代表论文的发表时间）