Study On The Key Technologies Of Acoustic Emission Signal Processing In Forecasting Rockburst

AE (Acoustic Emission, AE) is an important method in nondestructive monitoring evaluation, but there are still many problems in the popularity of AE forecasting rockburst. The main problems to be resolved are poor anti-noise performance, inaccurate location and forecasting which requires human intervention etc. Based on the analysis of the impact of the mechanism,this paper studys the following key problems from the perspective of AE signal processing.1. To obtain accurate AE signals which can represent mass characteristics of coal and rock is prerequisite to accurately predict the rockburst. The noise sources underground are multitudinous and complex, and have an environment with special rules. Based on the analysis of interference noise characteristics, the paper proposed to design a pair of adaptive filters to counteract the noise signal in ground pressure on the AE signal. The double adaptive filter involves two aspects: the adaptive filtering algorithms and adaptive non-uniform subband filter.2. Passive TDOA location method is mainly adapted in monitoring mine AE location and time delay is estimated to be the most crucial core question. The characteristics of AE signal propagation in coal and rock directly affect its localization algorithm. This paper constructs the physical model of the AE signal transmission in coal and rock for the first time, and establish mathematical model. Based on analysis of noise propagation characteristics and distribution, the paper proposes and designs time delay estimation method which combines with the wavelet analysis and the cross-correlation method.3. AE forecasting expert system can compensate for the shortage of qualified personnel of mine, improve forecast accuracy. The paper establishes the three layers model of rock prediction, combining with coal strata impact tendentiousness index measured in the laboratory and the AE monitoring data measured in the mine, and introduces the adaptive fuzzy neural network to train membership functions, overcomes the prediction error caused by individual. The feasibility and accuracy of this method has proved through data simulation.4. The layout of the sensor affects location accuracy of AE source positioning on one hand, on the other hand directly impacts on the system cost. The very different point between the arrangement of AE sensors in mines and other applications is that as the expansion of mining areas is more and more covered, the areas of being monitored is increasing as well, it must be taken into account that how to reach the best monitoring effect sensors adding a few sensor. Week signals were only detected by a fixed sensor which failed in accurate locations. In order to overcome this shortcoming, this paper proposes a wireless sensor network to complement the wired network, establishes the monitoring method for wireless sensor network monitoring rock orientation, analyzes and improves the positioning algorithm of wireless networks.