本文選題：支架　切入點：無線傳感器網(wǎng)絡(luò)　出處：《中國礦業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：不斷增大的煤礦采高對液壓支架的支護能力提出了更高的要求。初撐力和來壓值是液壓支架最重要的兩個指標(biāo),對煤礦安全開采具有現(xiàn)實指導(dǎo)意義。目前,液壓支架支設(shè)的初撐力和來壓值基本由支架操作人員主觀經(jīng)驗確定,使得很多支架在實際工作時的初撐力低于其設(shè)計值。因此本課題設(shè)計了一套基于無線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks,WSN)的工作阻力監(jiān)測系統(tǒng);通過對支架工作阻力曲線的分析,提出了一種支架初撐力和周期來壓值的自動提取方法;并利用極限學(xué)習(xí)理論實現(xiàn)了支架工作阻力的短期預(yù)測。研究內(nèi)容主要包括:首先,給出了基于WSN的支架工作阻力監(jiān)測系統(tǒng)的軟件設(shè)計方法。以Microsoft Visual Studio 2008、Microsoft SQL Server 2005為集成開發(fā)環(huán)境,利用C#和SQL語言實現(xiàn)了數(shù)據(jù)的接收、存儲、顯示、查詢、曲線繪制等功能。其次,提出了支架初撐力提取方法。首先,借鑒非均勻量化思想,利用A率壓縮算法對液壓支架工作阻力曲線進行非均勻量化。因為初撐力區(qū)的數(shù)據(jù)變化劇烈,必然會產(chǎn)生最多的量化段數(shù)。采用滑動窗口法找到量化段數(shù)最多的初撐力區(qū)。然后利用能量比法在初撐力區(qū)提取出初撐力值。最后將本文方法提取的初撐力值和經(jīng)驗估算值進行了對比,驗證了算法的有效性。最后,利用極限學(xué)習(xí)機(Extreme Learning Machine,ELM)理論把工作阻力歷史數(shù)據(jù)作為ELM的訓(xùn)練集樣本。通過分析隱含層神經(jīng)元個數(shù)對ELM性能的影響,本次實驗中將隱含層節(jié)點數(shù)定為20來確定ELM網(wǎng)絡(luò)的模式。通過實驗仿真,預(yù)測曲線與實際曲線擬合效果良好,除了在移架過程中的預(yù)測誤差超過了10%,預(yù)測輸出誤差率都在2%附近。
[Abstract]:The increasing mining height of coal mine has put forward higher requirements for the support ability of the hydraulic support. The initial support force and the pressure value are the two most important indexes of the hydraulic support, which have practical guiding significance for the safe mining of coal mine. The initial support force and pressure value of the hydraulic support are basically determined by the subjective experience of the support operator. This paper designs a set of working resistance monitoring system based on Wireless Sensor Networks (WSNs) of wireless sensor network, and analyzes the working resistance curve of the support. In this paper, an automatic method for extracting the initial support force and periodic pressure of the support is proposed, and the short-term prediction of the support working resistance is realized by using the limit learning theory. The main contents of the research are as follows: first of all, The software design method of support working resistance monitoring system based on WSN is presented. Taking Microsoft Visual Studio 2008 Microsoft SQL Server 2005 as the integrated development environment, the functions of data receiving, storing, displaying, querying, curve drawing and so on are realized by using C # and SQL language. In this paper, a method of extracting support initial support force is proposed. Firstly, using the idea of non-uniform quantization and A rate compression algorithm for non-uniform quantization of the working resistance curve of hydraulic support, because the data of the initial bracing force region change dramatically, The sliding window method is used to find the first bracing force region with the largest number of quantized segments. Then the initial bracing force is extracted from the initial bracing force region by using the energy ratio method. Finally, the sum of the initial bracing force values obtained by the method in this paper is given. The empirical estimates are compared, The validity of the algorithm is verified. Finally, the working resistance history data are taken as the training set samples of ELM by using extreme Learning Machine (ELM) theory. The effect of the number of hidden neurons on the performance of ELM is analyzed. In this experiment, the number of hidden layers is set at 20 to determine the model of ELM network. The simulation results show that the predicted curve fits well with the actual curve. With the exception of more than 10 prediction errors in the moving frame, the predicted output error rates are around 2%.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD355.4

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本文編號：1574562