The Prediction Of Coal And Gas Outburst Based On The Acoustic Emission Method

Coal mine safety problems are the key problems that the coal industry urgently needs toaddress. One of the most serious natural disasters in coal mines is coal and gas outburst. It is amajor factor of causing loss of life and affecting production efficiency. Coal and gas outburst arehighlighted in an apparent precursor. Through research on the gas outburst mechanism, thearticle proposed a new method-temperature judgment method. According to the propagationcharacteristics and the outburst risk index of acoustic emission in coal seam. Considered thecomplex of the signal, the signal extraction is the most important, According to thecharacteristics of the acoustic emission signal, Introduction the wavelet analysis theory. As amathematical tool for extraction and processing of non-stationary signal, Considered that the AEsignal usually mixes the noise signal, and is quite weak. Today’s noise cancellation technologycan not be very good for acoustic emission monitoring and can not be accurately forecast.Therefore this paper proposed acoustic emission signal de-noising methods based on waveletanalysis, The system design a new wavelet function as the basis functions of the BP neuralnetwork, and has proven the superiority of this thought through the experimental computationand computer simulation, and application in prediction of coal and gas outburst. On the basis ofthe previous chapter, the article design an acoustic emission signal processing and analysis tools-integrated acoustic emission signal processing platform and the article last chapter also brieflydescribes the development work of the signal processing platform and function of the module.