Removing The Noise From Acoustic Emission Signal Predicting Outburst By Adaptive Filter

This thesis probes into the feasibility of removing the working-noise from the signal of Acoustic Emission (AE) monitoring coal and gas outburst (outburst) using adaptive filter. It discusses the ways of predicting coal and gas outburst in the available coalmines and the significance of the untouched style of AE predicting outburst. The thesis discusses the filtering ways of available AE monitoring system & instrument used in coalmines to predict outburst, and analyzes the AE signal and working-noise of coalmines. Based on the analyzing result the thesis presents the removal of the underground working-noise by use of adaptive digital canceller. The thesis discusses and compares the advantage of all kinds of adaptive filtering algorithmic, and then selects the DCT-LMS adaptive algorithmic to remove the working-noise due to the correlation matrix of the working-noise being often rank deficiency. The optimal step size equation is derived according to LMS algorithmic. In light of the fact that the position of the resource of working-noise changes frequently and the acoustic characteristic of coalseam varies with the proceeding of mining, the thesis models the change of response function by single delay estimation in DCT domain to raise the filtering capability. The thesis analyzes the field working-noise with DEWE-3010 testing device, and experiments the working-noise with adaptive canceller. The simulation result shows the efficiency of DCT-LMS adaptive filter for removing the underground working-noise. Finally, the thesis considers the real-time problem of the filter and calculates the elapsed time. The result demonstrates that the type of TMS320F243 DSP can realize the real time filtering the working-noise. Summarizing the experimental result and the calculating result, it is feasible to remove the working-noise of Acoustic Emission monitoring the coal and gas outburst by the adaptive filter.