Research on Wavelet Denoising of AE Signal in Evolution Process of Coal and Gas Outburst
Applying AE technology to predict the gas outburst is the most promising method recognized by many researchers. However, there are a lot of noises in the Acoustic Emission(AE) signals collected in engineering practice that greatly affect the automatic identification of signal and the reliability of catastrophe prediction. In order to solve the problem, wavelet de-noising analysis was researched according to the characteristic of AE monitored. The wavelet time-frequency characteristic of AE signal in evolution process of coal and gas outburst was analyzed, and then wavelet denoising was proceeded. The results indicate that the wavelet base db6 in Daubechies wavelet family is an effective wavelet base for AE signal processing. Wavelet timefrequency analysis can identify effectively the frequency band of noise and effective signal in AE data. Wavelet analysis can denoise the signal effectively, and can improve signal-to-noise ratio of data.
Acoustic Emission Wavelet Analysis Denoise Time-Frequency Analysis
YANG Huiming WEN Guangcai LI Jiangong SUN Wenbin
College of Resource and Environment Engineering, Shandong University of Science and Technology, Qing Chongqing Institutes of China Coal Research Institute, Chongqing 400037, China College of Resource and Environment Engineering, Shandong University of Science and Technology, Qing
国际会议
2010 International Conference on Mine Hazards Prevention and Control(第二届矿山灾害预防与控制国际学术会议 ICMHPC)
青岛
英文
303-308
2010-10-15（万方平台首次上网日期，不代表论文的发表时间）