本文選題：煤與瓦斯突出 + 聲發(fā)射�。� 參考：《太原理工大學(xué)》2013年碩士論文

【摘要】：煤炭作為我國的第一能源,隨著經(jīng)濟(jì)的發(fā)展需求量與日俱增,伴隨而來的煤礦安全問題也不容忽視。煤與瓦斯突出是一種嚴(yán)重的礦井自然災(zāi)害,它雖具有突發(fā)性,但在突出前均有前兆顯現(xiàn),其中聲發(fā)射就是前兆之一。針對我國當(dāng)前的煤礦開采現(xiàn)狀,本文在聲發(fā)射理論的基礎(chǔ)上設(shè)計了一個煤與瓦斯突出的聲發(fā)射監(jiān)測儀,并通過仿真、調(diào)試,最終達(dá)到了較為理想的預(yù)期效果。本文主要從以下三個方面對監(jiān)測儀進(jìn)行了設(shè)計。 在煤礦井下,突出聲發(fā)射信號眾多但無明顯規(guī)律可循,而人工神經(jīng)網(wǎng)絡(luò)具有強(qiáng)大的非線性處理能力,它可以不用研究大量監(jiān)測數(shù)據(jù)之間的復(fù)雜關(guān)系,僅僅通過對輸入、輸出的記憶學(xué)習(xí)便可以找出相應(yīng)的非線性映射關(guān)系,這正適合于聲發(fā)射數(shù)據(jù)的動態(tài)預(yù)測。本文系統(tǒng)研究了BP神經(jīng)網(wǎng)絡(luò)在煤與瓦斯突出聲發(fā)射預(yù)測的基本原理,并論證了該設(shè)計的可行性。 在聲發(fā)射和BP神經(jīng)網(wǎng)絡(luò)的理論基礎(chǔ)上,設(shè)計了聲發(fā)射檢測儀的總體框架。根據(jù)煤與瓦斯突出預(yù)測快速、及時、準(zhǔn)確的要求,設(shè)計了一種基于DSP+單片機(jī)的雙CPU并行處理、計算的監(jiān)測系統(tǒng),巧妙了運(yùn)用了DSP對聲發(fā)射信號強(qiáng)大的數(shù)據(jù)處理能力和單片機(jī)的人機(jī)交互功能。 從硬件結(jié)構(gòu)出發(fā),根據(jù)突出聲發(fā)射數(shù)據(jù)監(jiān)測的要求詳細(xì)設(shè)計了系統(tǒng)中各個功能模塊。接著以硬件結(jié)構(gòu)為基礎(chǔ),對每一個模塊按其功能進(jìn)行了軟件編程,并進(jìn)行了調(diào)試。 最后在MATLAB軟件中利用神經(jīng)網(wǎng)絡(luò)工具箱搭建了聲發(fā)射信號BP神經(jīng)網(wǎng)絡(luò)處理的模型,并對相應(yīng)的數(shù)據(jù)進(jìn)行了學(xué)習(xí)訓(xùn)練,最后完成了仿真,并運(yùn)用遺傳算法對BP神經(jīng)網(wǎng)絡(luò)進(jìn)行了改進(jìn),其結(jié)果顯示,該方法能比較好的預(yù)測煤與瓦斯突出的危險性。
[Abstract]:Coal as the first energy in China, with the increasing demand for economic development, the coal mine safety problems can not be ignored. Coal and gas outburst is a serious mine natural disaster. Although it is sudden, it has the precursors before the outburst, and the acoustic emission is one of the precursors. In this paper, an acoustic emission monitor for coal and gas outburst is designed on the basis of acoustic emission theory, and the desired results are achieved through simulation and debugging. This paper mainly designs the monitor from the following three aspects.
In the coal mine, the outburst acoustic emission signals are numerous but have no obvious rules to follow, and the artificial neural network has a strong non-linear processing ability. It can not study the complex relationship between the large number of monitoring data, and only through the memory learning of input and output, it can find the corresponding nonlinear mapping relation, which is suitable for sound hair. The basic principle of BP neural network in predicting the acoustic emission of coal and gas outburst is systematically studied, and the feasibility of the design is demonstrated.
Based on the theory of acoustic emission and BP neural network, the overall framework of acoustic emission detector is designed. According to the fast, timely and accurate requirements of coal and gas outburst prediction, a dual CPU parallel processing based on DSP+ single chip computer is designed and the monitoring system is calculated. The powerful data processing capability of DSP to acoustic emission signals is skillfully carried out. The man-machine interactive function of the single chip microcomputer.
From the hardware structure, each function module in the system is designed in detail according to the requirements of the outburst of acoustic emission data monitoring. Then, based on the hardware structure, each module is programmed according to its function, and the debugging is carried out.
Finally, in MATLAB software, a neural network toolbox is used to build a model of acoustic emission signal BP neural network processing, and the corresponding data are studied and trained. Finally, the simulation is completed, and the genetic algorithm is used to improve the BP neural network. The results show that the method can predict the danger of coal and gas outburst. Sex.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TD713

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