本文選題：聲發(fā)射 + TBM�。� 參考：《石家莊鐵道大學(xué)》2013年碩士論文

【摘要】：滾動(dòng)軸承作為旋轉(zhuǎn)機(jī)械的關(guān)鍵部件之一,對設(shè)備的運(yùn)行安全影響重大。由于工作條件、尺寸設(shè)計(jì)、使用時(shí)間等因素的約束,其又是旋轉(zhuǎn)機(jī)械中最容易出現(xiàn)故障的零件之一,因此近些年來,針對滾動(dòng)軸承的狀態(tài)監(jiān)測和故障診斷技術(shù)成為了熱點(diǎn)話題。常用的振動(dòng)檢測技術(shù)在該領(lǐng)域已經(jīng)取得了較好的認(rèn)可度,但是受環(huán)境噪聲和信號衰減等因素的影響,傳統(tǒng)的振動(dòng)檢測技術(shù)和信號分析處理技術(shù)對于軸承早期的微弱故障診斷并不是很理想。相比下,聲發(fā)射檢測技術(shù)能夠更好的發(fā)現(xiàn)軸承早期故障特征,同時(shí)形態(tài)學(xué)的信號處理方法也較傳統(tǒng)的處理方法有自己的優(yōu)勢。 本文首介紹了聲發(fā)射檢測技術(shù)及數(shù)學(xué)形態(tài)學(xué)方法的國內(nèi)外的研究現(xiàn)狀。詳細(xì)闡述了聲發(fā)射檢測技術(shù)、聲發(fā)射信號的特點(diǎn)、聲發(fā)射信號的常用處理方法,同時(shí)介紹了數(shù)學(xué)形態(tài)學(xué)方法的產(chǎn)生發(fā)展情況及其基本數(shù)學(xué)原理。 本文選取重慶地鐵六號線銅鑼山隧道施工復(fù)合式TBM主軸承為研究對象,通過熟悉TBM主要結(jié)構(gòu)構(gòu)造,工作原理,作業(yè)方式等,設(shè)計(jì)了滾動(dòng)軸承聲發(fā)射試驗(yàn),進(jìn)行了聲發(fā)射信號采集試驗(yàn),并取得較理想的分析信號。根據(jù)信號的時(shí)域圖像特征及相關(guān)技術(shù)人員的數(shù)據(jù)支持,初步對TBM主軸承的工作狀態(tài)做了判斷。 本文研究了時(shí)變峭度法在軸承聲發(fā)射信號故障特征提取中的應(yīng)用,利用了峭度對沖擊信號敏感的特點(diǎn),將信號分段來計(jì)算峭度指標(biāo),繪制時(shí)變峭度圖,觀察峭度值走勢,來判別故障。通過對TBM實(shí)測信號的分析處理,結(jié)果證明了時(shí)變峭度分析法是一種快捷有效的軸承故障診斷方法。 本文研究了形態(tài)學(xué)方法在滾動(dòng)軸承故障信號處理中的應(yīng)用,通過構(gòu)造不同的形態(tài)算子和結(jié)構(gòu)元素,用多尺度形態(tài)濾波器實(shí)現(xiàn)了同時(shí)抑制噪聲和突出脈沖沖擊特征的目的。實(shí)驗(yàn)數(shù)據(jù)證明了形態(tài)學(xué)變換的信號分析方法的可行性及有效性。 本課題的研究對于TBM的狀態(tài)監(jiān)測及維護(hù)檢修具有指導(dǎo)性意義。
[Abstract]:As one of the key components of rotating machinery, rolling bearing has a great influence on the safety of the equipment. Due to the constraints of working conditions, dimension design and service time, it is one of the most prone parts in rotating machinery, so in recent years, the status monitoring and fault diagnosis technology for rolling bearings has become a hot topic. The commonly used vibration detection techniques have achieved good recognition in this field, but are affected by environmental noise and signal attenuation. Traditional vibration detection and signal analysis are not ideal for early weak fault diagnosis of bearings. Compared with the conventional methods, the acoustic emission detection technique can better detect the early fault features of bearings, and the morphological signal processing method also has its own advantages. In this paper, the present situation of acoustic emission detection and mathematical morphology is introduced. The acoustic emission detection technology, the characteristics of acoustic emission signals and the common processing methods of acoustic emission signals are described in detail. At the same time, the generation and development of mathematical morphology methods and their basic mathematical principles are introduced. In this paper, the composite TBM main bearing of Chongqing Metro Line 6 is selected as the research object, and the acoustic emission test of rolling bearing is designed by familiar with the main structure, working principle and operation mode of TBM. The acoustic emission signal acquisition test is carried out, and the ideal analysis signal is obtained. According to the time domain image feature of the signal and the data support of the related technicians, the working state of the TBM main bearing is preliminarily judged. In this paper, the application of time-varying kurtosis method in fault feature extraction of bearing acoustic emission signal is studied. By using kurtosis sensitivity to impact signal, the kurtosis index is calculated by the signal segment, the time-varying kurtosis chart is drawn, and the trend of kurtosis value is observed. To identify faults. By analyzing and processing the measured signals of TBM, it is proved that the time-varying kurtosis analysis method is a fast and effective method for bearing fault diagnosis. In this paper, the application of morphological method in fault signal processing of rolling bearing is studied. By constructing different morphological operators and structural elements, multi-scale morphological filter is used to simultaneously suppress noise and highlight impulse impulse characteristics. Experimental data show the feasibility and effectiveness of the signal analysis method based on morphological transformation. The research of this subject has instructive significance for condition monitoring and maintenance of TBM.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TH133.33;TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳春朝;李孟源;王恒迪;任煥琴;;鐵路貨車軸承的聲發(fā)射故障診斷及分析[J];軸承;2006年01期

2 安連鎖,胡愛軍,唐貴基,向玲;采用數(shù)學(xué)形態(tài)濾波器的軸心軌跡提純[J];動(dòng)力工程;2005年04期

3 王文波;羿旭明;費(fèi)浦生;;基于圖像統(tǒng)計(jì)特性的二維形態(tài)小波的構(gòu)造[J];光電子.激光;2008年09期

4 王雁軍;;TBM主軸承故障成因分析及維保技術(shù)[J];國防交通工程與技術(shù);2011年02期

5 向凡夫;施保昌;;基于斜變換的形態(tài)小波及其應(yīng)用[J];計(jì)算機(jī)工程與應(yīng)用;2006年13期

6 劉宏志;;TBM及盾構(gòu)機(jī)設(shè)備狀態(tài)監(jiān)測與故障診斷實(shí)用技術(shù)綜述[J];隧道建設(shè);2007年06期

7 章立軍;楊德斌;徐金梧;陳志新;;基于數(shù)學(xué)形態(tài)濾波的齒輪故障特征提取方法[J];機(jī)械工程學(xué)報(bào);2007年02期

8 郝如江;盧文秀;褚福磊;;滾動(dòng)軸承故障信號的多尺度形態(tài)學(xué)分析[J];機(jī)械工程學(xué)報(bào);2008年11期

9 李素琴;;TBM設(shè)備組成及功能介紹[J];科技情報(bào)開發(fā)與經(jīng)濟(jì);2007年13期

10 理華,徐春廣,肖定國,黃卉,鄭軍,季皖東,郭浩;小波包原理在滾動(dòng)軸承聲發(fā)射檢測技術(shù)中的應(yīng)用[J];機(jī)械;2002年04期

相關(guān)博士學(xué)位論文 前1條

1 任獲榮;數(shù)學(xué)形態(tài)學(xué)及其應(yīng)用[D];西安電子科技大學(xué);2004年

相關(guān)碩士學(xué)位論文 前1條

1 楊黎明;聲發(fā)射技術(shù)用于段修貨車軸承故障診斷研究[D];西南交通大學(xué);2003年

，

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