Acoustic emission signal noise filtering and danger comprehensive judgment method based on multi-system fusion

Abstract

The invention relates to an acoustic emission signal noise filtering and danger comprehensive judgment method based on multi-system fusion, and belongs to the technical field of coal rock dynamic disaster acoustic emission monitoring. The method comprises the steps that an underground coal mine acoustic emission monitor continuously monitors acoustic emission signals released in the coal and rockmass damage process, and collected original acoustic emission full-waveform signals are triggered to be uploaded to upper computer software through the Ethernet; upper computer software judges whetherthe acoustic emission signals are mixed with noise signals or not through multi-system fusion, compares noise samples in a noise library, recognizes and locates a noise source and filters noise influences. According to the invention, noise signal interference mixed in the acoustic emission signals can be accurately recognized and filtered out, and acoustic emission signal feature vector values are obtained; the acoustic emission signal analysis result is combined with the gas and roof monitoring result shared by the fusion system, real-time monitoring and comprehensive early warning of coal rock dynamic disasters can be achieved, and the early warning result is more reliable.

Description

technical field [0001] The invention belongs to the technical field of acoustic emission monitoring for coal-rock dynamic disasters, and relates to a noise-filtering and risk comprehensive judgment method for a coal mine acoustic emission monitoring system with multi-system fusion judgment. Background technique [0002] Coal-rock dynamic disaster is a rapid instability phenomenon with dynamic effects and disaster consequences that occurs in mine coal-rock mass under the action of in-situ stress and gas. An acoustic emission message is released. Through the continuous monitoring and analysis of the acoustic emission information released during the coal and rock mass failure process, the dynamic disaster prediction of the working face can be realized. [0003] Although our country has carried out many years of research in this area, due to the complex underground environment of coal mines and many types of noise, the same type of noise has great differences in different envir...

AI Technical Summary

Problems solved by technology

[0003] Although our country has carried out many years of research in this area, due to the complex underground environment of coal mines and many types of noise, the same type of noise has great differences in different environments, and the acoustic emission signal and noise signal are often mixed together, giving the acoustic emission Signal extraction brings great interference

The existing isolation and noise filtering technology can well isolate low-frequency environmental noise, filter out electrical noise, and filter out some mechanical noise through the known time and frequency characteristics of the noise signal, but for random human activity noise and most mechanical noise Noise, because it is difficult to quickly identify, the real-time filtering effect is not good, and it is difficult to obtain pure acoustic emission signals, which affects the accuracy and reliability of the analysis results

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