【摘要】：隨著我國經(jīng)濟(jì)建設(shè)和科學(xué)研究事業(yè)的進(jìn)一步發(fā)展，篩分機(jī)械設(shè)備所涉及的領(lǐng)域與應(yīng)用變得越來越廣泛，對(duì)于有原材料生產(chǎn)以及應(yīng)用的領(lǐng)域，都可以看到篩分機(jī)械設(shè)備，而在這些篩分機(jī)械設(shè)備中，最常見和常用的設(shè)備就是振動(dòng)篩。在煤炭工業(yè)部門、水利水電部門、交通工業(yè)部門、化工部門甚至在環(huán)衛(wèi)部門都已經(jīng)應(yīng)用到了振動(dòng)篩。可以看出振動(dòng)篩在各個(gè)行業(yè)部門起著至關(guān)重要的作用。而振動(dòng)篩的軸承部分對(duì)于振動(dòng)篩的正常工作有著重要的作用，其工況不僅影響該機(jī)器設(shè)備本身的安全穩(wěn)定運(yùn)行，而且還會(huì)對(duì)后續(xù)生產(chǎn)造成直接影響，，故障嚴(yán)重時(shí)會(huì)造成重大經(jīng)濟(jì)損失，甚至造成機(jī)毀人亡的事故，因此對(duì)軸承進(jìn)行故障檢驗(yàn)技術(shù)與分析技術(shù)顯得更加迫切。 故障診斷技術(shù)是一門新發(fā)展的科學(xué)領(lǐng)域，還沒有形成較為完整的科學(xué)體系。因此對(duì)研究的目的、內(nèi)容范疇的理解，往往與工程應(yīng)用背景，乃至工程技術(shù)人員的專業(yè)不同而有很大的差異，所以對(duì)現(xiàn)有的故障理論方法還有一些不足之處與難題，而最關(guān)鍵也是最困難的問題之一就是故障特征信號(hào)的特征提取。可以這么說，特征提取是當(dāng)前故障診斷方面中的一個(gè)瓶頸問題，它對(duì)于故障診斷的準(zhǔn)確性和早期預(yù)報(bào)的可靠性有著很大的關(guān)系。而盲源分離理論為振動(dòng)信號(hào)的處理、故障診斷的識(shí)別提供了積極地方法。 但是正如其他算法一樣，它也有自身的限制，其一就是觀測數(shù)必須大于振動(dòng)源數(shù)，如果不能滿足這一前提條件，那么分離最終會(huì)造成失敗。針對(duì)這一限制，本文提出了基于集合平均經(jīng)驗(yàn)?zāi)B(tài)分解的盲源分離算法(EEMD-BSS)，該算法能很好的克制這一限制，使得在觀測數(shù)小于振動(dòng)源數(shù)的情況下也能較好的分離出故障數(shù)據(jù)，從而達(dá)到分離的目的。 最后本文分別使用傳統(tǒng)的盲源分離算法和改進(jìn)的EEMD-BSS算法對(duì)軸承的內(nèi)外圈實(shí)驗(yàn)故障數(shù)據(jù)進(jìn)行了多通道與單通道的故障特征的分離，都較好的完成了分離任務(wù)，說明算法的有效性。
[Abstract]:With the further development of economic construction and scientific research in our country, the fields and applications of screening machinery and equipment have become more and more extensive. For the fields where raw materials are produced and applied, we can see screening machinery and equipment. In these screening mechanical equipment, the most common and commonly used equipment is vibrating screen. Shakers have been used in coal, hydropower, transportation, chemicals and even sanitation. It can be seen that the vibrating screen plays a vital role in all sectors of the industry. The bearing part of the vibrating screen plays an important role in the normal operation of the vibrating screen. Its working conditions not only affect the safe and stable operation of the machine itself, but also have a direct impact on the subsequent production. When the fault is serious, it will cause great economic loss, even cause the accident of machine destruction and death, so it is more urgent to carry on the fault inspection and analysis technology to the bearing. Fault diagnosis technology is a newly developed field of science and has not yet formed a relatively complete scientific system. Therefore, the understanding of the purpose and content category of the research is often different from the engineering application background and even the engineering technicians' specialty, so there are still some deficiencies and difficulties in the existing fault theory and methods. One of the most critical and difficult problems is feature extraction of fault feature signals. It can be said that feature extraction is a bottleneck problem in fault diagnosis at present. It has a great relationship with the accuracy of fault diagnosis and the reliability of early prediction. Blind source separation theory provides an active method for vibration signal processing and fault diagnosis. However, like other algorithms, it has its own limitations. One is that the number of observations must be greater than the number of vibration sources. If this precondition is not satisfied, separation will eventually lead to failure. In order to overcome this limitation, a blind source separation algorithm (EEMD-BSS) based on set average empirical mode decomposition (EMD) is proposed in this paper. The fault data can be separated better when the number of observations is less than the number of vibration sources, so as to achieve the purpose of separation. Finally, the traditional blind source separation algorithm and the improved EEMD-BSS algorithm are used to separate the multi-channel and single-channel fault data of the bearing's inner and outer ring experiment respectively. The effectiveness of the algorithm is illustrated.
【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 焦衛(wèi)東;朱有劍;;基于EMD的軸承故障包絡(luò)譜分析[J];軸承;2009年01期

2 朱孝龍,保錚,張賢達(dá);基于分階段學(xué)習(xí)的盲信號(hào)分離[J];中國科學(xué)E輯:技術(shù)科學(xué);2002年05期

3 毋文峰;陳小虎;蘇勛家;;基于經(jīng)驗(yàn)?zāi)Ｊ椒纸獾膯瓮ǖ罊C(jī)械信號(hào)盲分離[J];機(jī)械工程學(xué)報(bào);2011年04期

4 王新文,張永忠,馬富強(qiáng);振動(dòng)篩滾動(dòng)軸承受力分析[J];礦山機(jī)械;1998年07期

5 時(shí)世晨;單佩韋;;基于EEMD的信號(hào)處理方法分析和實(shí)現(xiàn)[J];現(xiàn)代電子技術(shù);2011年01期

6 李寧;史鐵林;;基于非負(fù)矩陣分解的盲信號(hào)源數(shù)估計(jì)[J];中國機(jī)械工程;2007年22期

相關(guān)會(huì)議論文 前1條

1 何振亞;楊綠溪;劉琚;魯子奕;何晨;;語音信號(hào)盲分離的多變量密度估計(jì)方法[A];1999年中國神經(jīng)網(wǎng)絡(luò)與信號(hào)處理學(xué)術(shù)會(huì)議論文集[C];1999年

相關(guān)博士學(xué)位論文 前1條

1 葉紅仙;機(jī)械系統(tǒng)振動(dòng)源的盲分離方法研究[D];浙江大學(xué);2008年

相關(guān)碩士學(xué)位論文 前2條

1 賈濤;振動(dòng)篩的故障診斷及動(dòng)力學(xué)分析[D];西安建筑科技大學(xué);2011年

2 王春花;振動(dòng)篩結(jié)構(gòu)損傷故障診斷[D];太原理工大學(xué);2006年

本文編號(hào)：2385789