本文選題：磨機(jī)負(fù)荷 + 特征提取��； 參考：《江西理工大學(xué)》2017年碩士論文

【摘要】：球磨機(jī)具有操作簡(jiǎn)單、制造成本低、破碎比大、既可用于濕磨又可用于干磨等諸多優(yōu)點(diǎn),廣泛應(yīng)用在玻璃、陶瓷、水泥、化工、礦山等領(lǐng)域。但是,球磨機(jī)磨礦過程存在多變量相互制約、強(qiáng)耦合、滯后時(shí)間長(zhǎng)等缺點(diǎn),造成其筒體內(nèi)部負(fù)荷參數(shù)無法顯性描述和實(shí)時(shí)控制,難于充分發(fā)揮磨機(jī)的實(shí)際效能。因此,實(shí)現(xiàn)磨機(jī)內(nèi)部負(fù)荷的有效預(yù)測(cè),使球磨機(jī)運(yùn)行在最佳工況狀態(tài),是提高磨礦效率、降低生產(chǎn)成本的根本任務(wù)之一。本文以試驗(yàn)球磨機(jī)為研究對(duì)象,通過經(jīng)驗(yàn)分析、實(shí)驗(yàn)探究、信號(hào)處理相結(jié)合的方法,采用多種傳感器分別檢測(cè)球磨機(jī)軸承振動(dòng)信號(hào)、筒體磨音信號(hào)和主電機(jī)電流信號(hào),應(yīng)用最優(yōu)融合集和D-S證據(jù)理論的多源信息融合技術(shù),對(duì)磨機(jī)負(fù)荷的多源信號(hào)特征提取與預(yù)測(cè)方法進(jìn)行了深入研究,實(shí)現(xiàn)了磨機(jī)內(nèi)部負(fù)荷狀態(tài)參數(shù)的有效預(yù)測(cè)。主要研究結(jié)果為:首先,針對(duì)球磨機(jī)耗能高、產(chǎn)量低、噪音大等問題,通過經(jīng)驗(yàn)分析得出磨礦過程的主要影響因素和信號(hào)檢測(cè)方法;搭建了球磨機(jī)多源信號(hào)檢測(cè)系統(tǒng),采用單因素變量法進(jìn)行了磨礦實(shí)驗(yàn),以加球量、給料量、入料粒度分布、球配比為輸入?yún)?shù),以能耗和-200目產(chǎn)率為評(píng)價(jià)指標(biāo),相關(guān)實(shí)驗(yàn)結(jié)果表明,不同磨機(jī)負(fù)荷參數(shù)可劃分為欠負(fù)荷、正常負(fù)荷、過負(fù)荷三種狀態(tài)。其次,為了對(duì)三種狀態(tài)的多源信號(hào)進(jìn)行特征提取和識(shí)別,采用了小波變換技術(shù)分別對(duì)球磨機(jī)振動(dòng)、磨音信號(hào)進(jìn)行特征提取,得到振動(dòng)特征信息為信號(hào)的均值、方差和頻率段能量值,磨音特征信息為信號(hào)的A計(jì)權(quán)總聲壓級(jí)和A計(jì)權(quán)倍頻程聲壓級(jí);通過對(duì)比分析不同工況下信號(hào)特征信息值的歐氏距離,結(jié)果表明與單一信號(hào)相比,多源信號(hào)能更準(zhǔn)確、更快速的對(duì)磨機(jī)負(fù)荷進(jìn)行識(shí)別;通過對(duì)不同時(shí)間段的電流信號(hào)進(jìn)行均值化處理,得出隨著磨機(jī)負(fù)荷的增加,電流值呈先增加后減小的趨勢(shì)。最后,針對(duì)磨機(jī)負(fù)荷預(yù)測(cè)中的檢測(cè)信號(hào)存在高沖突、強(qiáng)突變、低相關(guān)的問題,采用了改進(jìn)后的最優(yōu)融合集算法,對(duì)同類信號(hào)在不同時(shí)間段的檢測(cè)數(shù)據(jù)進(jìn)行融合,結(jié)果表明該方法能有效剔除高沖突信息;采用改進(jìn)后的D-S證據(jù)理論融合規(guī)則,提出了一種磨機(jī)負(fù)荷的多源異類信號(hào)特征層融合方法,并通過實(shí)例驗(yàn)證和不同算法對(duì)比分析,表明該方法應(yīng)用于磨機(jī)負(fù)荷預(yù)測(cè)時(shí),得到的融合結(jié)果置信度更高、收斂速度更快、穩(wěn)定性更強(qiáng)。綜上所述,通過單因素變量磨礦試驗(yàn)和多源信號(hào)特征提取與識(shí)別,采用最優(yōu)融合集和D-S證據(jù)理論建立多源信號(hào)特征層融合方法,對(duì)磨機(jī)負(fù)荷預(yù)測(cè)具有較強(qiáng)的實(shí)用性及可靠性,也可為其它選礦設(shè)備的節(jié)能降耗提供設(shè)計(jì)新思路。
[Abstract]:Ball mill has many advantages, such as simple operation, low manufacturing cost, large crushing ratio, and can be used in wet grinding and dry grinding. It is widely used in glass, ceramics, cement, chemical industry, mine and so on. However, the grinding process of ball mill has many disadvantages, such as multi-variable mutual restriction, strong coupling, long lag time and so on. As a result, the internal load parameters of ball mill can not be explicitly described and real-time controlled, and it is difficult to give full play to the actual efficiency of the mill. Therefore, it is one of the fundamental tasks to improve the grinding efficiency and reduce the production cost to realize the effective forecasting of the internal load of the mill and to make the ball mill run in the best working condition. This paper takes the test ball mill as the research object, through the experience analysis, the experiment inquiry, the signal processing method, uses the many kinds of sensors separately detects the ball mill bearing vibration signal, the cylinder body grinding sound signal and the main motor current signal. Based on the optimal fusion set and the multi-source information fusion technique of D-S evidence theory, the multi-source signal feature extraction and prediction method of the mill load is deeply studied, and the effective prediction of the internal load state parameters of the mill is realized. The main results are as follows: firstly, aiming at the problems of high energy consumption, low output and high noise, the main influencing factors and signal detection methods of grinding process are obtained by empirical analysis, and the multi-source signal detection system of ball mill is built. The grinding experiments were carried out by single factor variable method. The parameters of ball addition, feed rate, particle size distribution, ball ratio, energy consumption and -200 mesh yield were used as input parameters. Different mill load parameters can be divided into three states: underload, normal load and overload. Secondly, in order to extract and recognize the features of multi-source signals in three states, wavelet transform technology is used to extract the feature of ball mill vibration and grinding sound signal respectively, and the characteristic information of vibration is obtained as the mean value of the signal. Variance and energy value of frequency band, A weighted total sound pressure level of signal and A weighted frequency doubling range sound pressure level, Euclidean distance of signal characteristic information under different working conditions are analyzed, the results show that compared with single signal, The multi-source signal can identify the mill load more accurately and quickly. Through the average processing of the current signal in different time period, it is concluded that the current value increases first and then decreases with the increase of mill load. Finally, aiming at the problems of high conflict, strong mutation and low correlation in the detection signal of mill load forecasting, the improved optimal fusion set algorithm is adopted to fuse the detection data of the same kind of signal in different time periods. The results show that this method can effectively eliminate the high conflict information, adopt the improved D-S evidence theory fusion rule, propose a multi-source and heterogeneous signal feature layer fusion method for mill load, and verify it by an example and compare different algorithms. It is shown that the proposed method has higher confidence, faster convergence speed and stronger stability when it is applied to mill load forecasting. To sum up, through single-factor variable grinding test and multi-source signal feature extraction and recognition, the optimal fusion set and D-S evidence theory are adopted to establish multi-source signal feature layer fusion method, which has strong practicability and reliability for mill load forecasting. It can also provide a new design idea for energy saving and consumption reduction of other mineral processing equipment.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD453

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 羅小燕;陳慧明;盧小江;熊洋;;基于網(wǎng)格搜索與交叉驗(yàn)證的SVM磨機(jī)負(fù)荷預(yù)測(cè)[J];中國(guó)測(cè)試;2017年01期

2 羅小燕;盧小江;熊洋;楊麗榮;;小波分析球磨機(jī)軸承振動(dòng)信號(hào)特征提取方法[J];噪聲與振動(dòng)控制;2016年01期

3 王飛;李智勇;朱強(qiáng);;基于自適應(yīng)閾值小波分析的磨音信號(hào)去噪[J];礦山機(jī)械;2015年12期

4 梁禮明;肖盈丁;吳健;;多輸入多輸出LSSVM磨機(jī)負(fù)荷軟測(cè)量[J];煤礦機(jī)械;2015年11期

5 李琳;張永祥;劉樹勇;;改進(jìn)EMD-小波分析的轉(zhuǎn)子振動(dòng)信號(hào)去噪方法[J];噪聲與振動(dòng)控制;2015年02期

6 于奇;王學(xué)彬;;滾動(dòng)軸承在球磨機(jī)中的應(yīng)用[J];科技創(chuàng)新與應(yīng)用;2015年05期

7 楊志剛;張杰;李艷姣;;磨音影響因素分析與磨機(jī)負(fù)荷檢測(cè)方法綜述[J];金屬礦山;2015年02期

8 徐兵強(qiáng);;提高球磨機(jī)磨礦效率技術(shù)措施[J];現(xiàn)代礦業(yè);2014年11期

9 吳光文;王昌明;包建東;陳勇;胡揚(yáng)坡;;基于自適應(yīng)閾值函數(shù)的小波閾值去噪方法[J];電子與信息學(xué)報(bào);2014年06期

10 賀曉巧;王建民;趙曄;;基于多信息融合的磨機(jī)負(fù)荷動(dòng)態(tài)尋優(yōu)控制[J];自動(dòng)化與儀表;2014年05期

相關(guān)博士學(xué)位論文 前6條

1 馬天雨;鋁土礦連續(xù)磨礦過程建模與優(yōu)化控制研究[D];中南大學(xué);2012年

2 王曉麗;鋁土礦連續(xù)球磨過程建模與關(guān)鍵參數(shù)優(yōu)化[D];中南大學(xué);2011年

3 羅春梅;球磨機(jī)節(jié)能降耗新途徑機(jī)理及應(yīng)用研究[D];昆明理工大學(xué);2009年

4 李勇;磨礦過程參數(shù)軟測(cè)量與綜合優(yōu)化控制的研究[D];大連理工大學(xué);2006年

5 王欣;多傳感器數(shù)據(jù)融合問題的研究[D];吉林大學(xué);2006年

6 yだ蠣，

本文編號(hào)：1868434