本文關(guān)鍵詞： 數(shù)據(jù)融合 煤礦粉塵 實(shí)時(shí)監(jiān)測 D-S證據(jù)理論 RS理論　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著煤礦生產(chǎn)自動(dòng)化深入推進(jìn),礦井產(chǎn)塵呈現(xiàn)出量大繁雜、管控難的問題。作為目前較為先進(jìn)的降塵裝置,煤礦粉塵在線監(jiān)測系統(tǒng)雖然實(shí)現(xiàn)了粉塵濃度實(shí)時(shí)連續(xù)監(jiān)測,但由于監(jiān)測指標(biāo)單一、數(shù)據(jù)信息冗錯(cuò)高的缺陷導(dǎo)致其決策失真、誤動(dòng)作頻發(fā),嚴(yán)重影響到除塵效率,同時(shí)也造成水資源極大浪費(fèi)。因此,本文將兩級(jí)數(shù)據(jù)融合算法應(yīng)用于煤礦粉塵監(jiān)測系統(tǒng)。利用數(shù)據(jù)層(第一級(jí))融合算法對同質(zhì)傳感器原始數(shù)據(jù)進(jìn)行過濾剔除和消除錯(cuò)誤冗余信息,并在分析基于D-S證據(jù)理論的算法和基于RS理論的算法這兩種決策層融合算法單一應(yīng)用不足的基礎(chǔ)上,提出了綜合D-S證據(jù)理論和RS理論的決策層新算法。從理論分析的角度證明了算法可行性,并開展了不同工礦位置的現(xiàn)場應(yīng)用。主要研究內(nèi)容概括如下:(1)在分析煤礦粉塵在線監(jiān)測系統(tǒng)中傳感器監(jiān)測指標(biāo)選取和數(shù)據(jù)處理的基礎(chǔ)上,將傳感器監(jiān)測指標(biāo)豐富細(xì)化為全塵濃度、呼塵濃度、粉塵粒度、風(fēng)速四項(xiàng),確保了除塵決策的全面有效。建立煤礦粉塵監(jiān)測數(shù)據(jù)庫,為兩級(jí)數(shù)據(jù)融合算法在煤礦粉塵監(jiān)測系統(tǒng)中的應(yīng)用奠定了基礎(chǔ)。(2)為適應(yīng)礦井環(huán)境復(fù)雜、工況多變的監(jiān)測環(huán)境,建立了數(shù)據(jù)融合算法的兩級(jí)結(jié)構(gòu)模型,明確了算法的層次和地位。數(shù)據(jù)層融合選取基于支持度矩陣的算法,在分析闡明算法主要思想的同時(shí)概括提煉出清晰的算法步驟,并以粉塵監(jiān)測數(shù)據(jù)庫數(shù)據(jù)為對象進(jìn)行了算例驗(yàn)證。在分析基于D-S證據(jù)理論的算法和基于RS理論的算法這兩種決策層融合算法的前提下,通過煤礦粉塵監(jiān)測中的應(yīng)用算例對兩種算法的決策結(jié)果進(jìn)行了對比,發(fā)現(xiàn)這兩種算法決策規(guī)則單一等問題往往導(dǎo)致決策結(jié)果不夠準(zhǔn)確,可信度不高。(3)提出了綜合D-S證據(jù)理論和RS理論的決策層新算法,從理論分析的角度證明了算法可行性,針對新算法實(shí)現(xiàn)過程中的三個(gè)關(guān)鍵問題(BPA函數(shù)獲取、屬性重要度評估、證據(jù)合成方法)做了重點(diǎn)分析,進(jìn)一步完善了新算法的理論基礎(chǔ)和運(yùn)算規(guī)則。最后,以相同監(jiān)測信息的處理為例證明了新算法比單一基于D-S證據(jù)理論的算法或基于RS理論的算法更具優(yōu)勢。它不僅可以客觀確定BPA函數(shù),以概率形式輸出決策結(jié)果,而且在粉塵決策精確度和可信度方面均有明顯提升。(4)對兩級(jí)數(shù)據(jù)融合算法在煤礦粉塵監(jiān)測中的應(yīng)用進(jìn)行了實(shí)例驗(yàn)證,然后在試驗(yàn)煤礦中以粉塵控制和節(jié)水效果為參考,研究測試了兩級(jí)數(shù)據(jù)融合算法對煤礦粉塵監(jiān)測系統(tǒng)的改進(jìn)效果。結(jié)果表明,在確保粉塵控制效果的前提下,改進(jìn)后系統(tǒng)噴霧裝置工作時(shí)間較改進(jìn)前平均減少了30%左右,噴水量平均下降了20%左右�，F(xiàn)場應(yīng)用結(jié)果表明數(shù)據(jù)融合算法的應(yīng)用對粉塵在線監(jiān)測系統(tǒng)的功能和效率有了明顯改善提升,驗(yàn)證了本文所提方法是正確可行的。
[Abstract]:With the further development of coal mine production automation, the problem of large and complicated quantity and difficult control of mine dust production appears. As a relatively advanced dust control device, the on-line monitoring system of coal mine dust has realized the real-time and continuous monitoring of dust concentration. However, the defects of single monitoring index and high redundancy of data information lead to the decision distortion and frequent misoperation, which seriously affect the efficiency of dust removal, and also cause a great waste of water resources. In this paper, the two-level data fusion algorithm is applied to the coal mine dust monitoring system. The data layer (first level) fusion algorithm is used to filter and remove the original data of the homogeneous sensor and eliminate the error redundancy information. Based on the analysis of the single application of the two decision level fusion algorithms based on D-S evidence theory and RS theory, A new decision layer algorithm based on D-S evidence theory and RS theory is proposed. The feasibility of the algorithm is proved from the point of view of theoretical analysis. The main research contents are summarized as follows: (1) on the basis of analyzing the selection of sensor monitoring indexes and data processing in online monitoring system of coal mine dust, The rich monitoring index of sensor is divided into four items: total dust concentration, exhaling dust concentration, dust particle size and wind speed, which ensures the overall effectiveness of dust removal decision. The coal mine dust monitoring database is established. For the application of two-level data fusion algorithm in coal mine dust monitoring system, a two-level structure model of data fusion algorithm is established to adapt to the complex and changeable monitoring environment of mine environment. The level and position of the algorithm are defined. The data layer fusion selects the algorithm based on support matrix, and summarizes the clear algorithm steps while analyzing and clarifying the main ideas of the algorithm. Taking the data of dust monitoring database as an example, the paper analyzes the two decision level fusion algorithms based on D-S evidence theory and RS theory. The decision results of the two algorithms are compared through the application examples in coal mine dust monitoring. It is found that the decision results of the two algorithms are often inaccurate due to the single decision rules of the two algorithms. In this paper, a new decision layer algorithm based on D-S evidence theory and RS theory is proposed. The feasibility of the algorithm is proved from the point of view of theoretical analysis. In view of the three key problems in the implementation of the new algorithm, BPA function acquisition and attribute importance evaluation are discussed. The method of evidence synthesis) makes the emphasis analysis, further consummates the new algorithm the theory foundation and the operation rule. Finally, Taking the processing of the same monitoring information as an example, it is proved that the new algorithm is superior to the single one based on D-S evidence theory or RS theory. It can not only objectively determine the BPA function, but also output the decision result in the form of probability. The application of two-level data fusion algorithm in coal mine dust monitoring is verified by an example, and then the dust control and water-saving effect are used as references in the experimental coal mine. The improvement effect of two-stage data fusion algorithm on coal mine dust monitoring system is studied and tested. The results show that the working time of the improved spray system is about 30% less than that before the improvement, on the premise of ensuring the dust control effect. The field application results show that the application of the data fusion algorithm has improved the function and efficiency of the dust on-line monitoring system obviously, and verified that the method proposed in this paper is correct and feasible.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD714.3;TP202

【參考文獻(xiàn)】

中國期刊全文數(shù)據(jù)庫 前10條

1 陳曦;葛少成;徐耀松;樊文濤;葛斐;;大型選煤廠粉塵在線監(jiān)測與智能噴霧降塵系統(tǒng)研究[J];安全與環(huán)境學(xué)報(bào);2015年05期

2 郝葉軍;;光散射性礦用粉塵傳感器的設(shè)計(jì)及應(yīng)用[J];煤礦機(jī)電;2015年05期

3 何國家;楊利峰;龍如銀;祁慧;陳紅;;煤礦粉塵危害風(fēng)險(xiǎn)規(guī)模評價(jià)模型[J];中國煤炭;2015年08期

4 肖丹;;煤礦井下粉塵超標(biāo)原因分析及其綜合管理探討[J];煤礦現(xiàn)代化;2015年04期

5 鄭雷;王雪芹;;煤礦粉塵爆炸事故樹分析[J];中國礦山工程;2015年03期

6 姜家興;王德明;;礦塵濃度監(jiān)測技術(shù)現(xiàn)階段存在的問題及改進(jìn)措施[J];煤炭技術(shù);2015年01期

7 張久紅;邵春華;葛憲民;;研究探討塵肺病的綜合預(yù)防與治療[J];職業(yè)衛(wèi)生與病傷;2014年05期

8 許滿貴;劉欣凱;文新強(qiáng);何鵬程;朱兆偉;;煤礦綜采工作面粉塵分布及運(yùn)移規(guī)律研究[J];西安科技大學(xué)學(xué)報(bào);2014年05期

9 周ml皋;;呼吸性粉塵分離效能測試方法誤差分析[J];礦業(yè)安全與環(huán)保;2013年06期

10 李瓊;楊曉祥;;多傳感器數(shù)據(jù)融合模型的研究[J];計(jì)算機(jī)與現(xiàn)代化;2013年04期

中國博士學(xué)位論文全文數(shù)據(jù)庫 前2條

1 韓磊;華東某煤礦粉塵污染變化趨勢、煤工塵肺發(fā)病特征及其預(yù)測研究[D];南京醫(yī)科大學(xué);2016年

2 崔波;多傳感器目標(biāo)跟蹤數(shù)據(jù)融合關(guān)鍵技術(shù)研究[D];西南交通大學(xué);2012年

中國碩士學(xué)位論文全文數(shù)據(jù)庫 前10條

1 柴倬君;雙Pt礦用風(fēng)速傳感器的研究[D];東華理工大學(xué);2016年

2 李鳳娟;數(shù)據(jù)融合技術(shù)在基于物聯(lián)網(wǎng)的火災(zāi)探測系統(tǒng)中的應(yīng)用研究[D];吉林大學(xué);2015年

3 王力;基于DS證據(jù)理論的多傳感器數(shù)據(jù)融合算法研究與應(yīng)用[D];太原理工大學(xué);2015年

4 解春香;異類多傳感器數(shù)據(jù)融合技術(shù)的研究[D];沈陽理工大學(xué);2015年

5 樊文濤;塔山選煤廠粉塵在線監(jiān)測與治理技術(shù)研究[D];遼寧工程技術(shù)大學(xué);2015年

6 劉小貝;粉塵微粒監(jiān)測系統(tǒng)的研究與設(shè)計(jì)[D];長春工業(yè)大學(xué);2014年

7 敖蕾蕾;基于D-S證據(jù)理論的電網(wǎng)設(shè)備狀態(tài)檢修決策研究[D];浙江大學(xué);2014年

8 張麗;中國主要城市空氣質(zhì)量與經(jīng)濟(jì)發(fā)展的關(guān)系研究[D];華北水利水電大學(xué);2013年

9 石西慶;基于數(shù)據(jù)融合技術(shù)的電子政務(wù)信息共享服務(wù)平臺(tái)模型[D];電子科技大學(xué);2013年

10 胡雅馨;基于粗糙集與證據(jù)理論的瓦斯傳感器故障診斷技術(shù)的研究[D];遼寧工程技術(shù)大學(xué);2009年

，

本文編號(hào)：1553413