【摘要】：滾動(dòng)軸承作為旋轉(zhuǎn)機(jī)械設(shè)備中最為常用的關(guān)鍵零部件之一,其運(yùn)轉(zhuǎn)狀況往往直接關(guān)系到整臺(tái)設(shè)備的安全穩(wěn)定運(yùn)行,一旦產(chǎn)生故障,將會(huì)極大的影響機(jī)械設(shè)備的生產(chǎn)安全和效率。因此,對(duì)滾動(dòng)軸承的損傷狀態(tài)進(jìn)行監(jiān)測(cè)診斷就顯得尤為重要。低速重載軸承由于其運(yùn)轉(zhuǎn)和本身結(jié)構(gòu)的復(fù)雜特殊,對(duì)于這類軸承的損傷狀態(tài)進(jìn)行監(jiān)測(cè)異常困難。隨著機(jī)械制造行業(yè)的快速發(fā)展,低速重載軸承的實(shí)際應(yīng)用范疇也越來(lái)越廣泛,并且這類軸承一般都安裝在大中型的旋轉(zhuǎn)機(jī)械設(shè)備中,一旦產(chǎn)生損壞造成停機(jī),其維修更換需要大量的人力物力財(cái)力,因此提前監(jiān)測(cè)這類軸承的損傷狀態(tài)能夠避免停機(jī)事故,獲得較大的經(jīng)濟(jì)效益。聲發(fā)射技術(shù)(AE)是一種新型的動(dòng)態(tài)實(shí)時(shí)監(jiān)測(cè)技術(shù),與傳統(tǒng)的檢測(cè)技術(shù)相比,聲發(fā)射信號(hào)對(duì)動(dòng)態(tài)缺陷敏感、頻帶較寬,檢測(cè)效率高,可以及時(shí)的發(fā)現(xiàn)低速重載滾動(dòng)軸承的早期損傷,對(duì)于旋轉(zhuǎn)機(jī)械設(shè)備中軸承的保養(yǎng)和維修具有重要的工程應(yīng)用價(jià)值。本文以聲發(fā)射檢測(cè)技術(shù)為手段,通過(guò)搭建實(shí)驗(yàn)臺(tái)來(lái)模擬低速重載軸承的運(yùn)行狀態(tài),對(duì)軸承預(yù)制不同位置和大小的人工缺陷,采集不同損傷狀態(tài)軸承的聲發(fā)射信號(hào),對(duì)其在低速軸承故障診斷和損傷狀態(tài)監(jiān)測(cè)的可行性進(jìn)行了理論和實(shí)驗(yàn)研究。完成的主要工作和成果有:借助試驗(yàn)臺(tái)采集不同損傷狀態(tài)的軸承聲發(fā)射信號(hào),分別采用小波分析和小波包分析對(duì)信號(hào)進(jìn)行分析處理,通過(guò)提取各頻帶所占能量百分比,得出相比小波分析,小波包分析能夠提取到軸承故障聲發(fā)射信號(hào)產(chǎn)生的主要頻帶,提取的能量較高的頻帶與頻譜圖高幅值頻帶相一致。并比較了小波尺度譜和STFT譜對(duì)低速軸承AE信號(hào)中的特征提取性能,結(jié)果表明小波尺度譜對(duì)于非平穩(wěn)聲發(fā)射信號(hào)的時(shí)間分辨率較高,而STFT譜相比小波尺度譜對(duì)于非平穩(wěn)信號(hào)的頻率分辨率較高,因而可以將小波尺度譜和STFT譜相結(jié)合用于低速軸承故障特征提取。針對(duì)低速軸承故障特征微弱,易被噪聲淹沒(méi),提出了結(jié)合能量熵和集合經(jīng)驗(yàn)?zāi)B(tài)分解(EEMD)進(jìn)行低速軸承故障診斷,并提出基于相關(guān)系數(shù)法和方差貢獻(xiàn)率法篩選有效本征模態(tài)分量。通過(guò)實(shí)驗(yàn)結(jié)果表明,采用互相關(guān)系數(shù)和方差貢獻(xiàn)率能夠篩選有效的IMF分量,提取的有效IMF能量熵能夠很好的表征低速軸承的損傷缺陷變化。并對(duì)比了支持向量機(jī)和BP神經(jīng)網(wǎng)絡(luò)對(duì)低速軸承的故障類型的分類識(shí)別效果,得出針對(duì)低速軸承小樣本數(shù)據(jù)支持向量機(jī)的識(shí)別準(zhǔn)確率要高于BP神經(jīng)網(wǎng)絡(luò)。
[Abstract]:Rolling bearing is one of the most commonly used key parts in rotating machinery. Its running condition is often directly related to the safe and stable operation of the whole equipment. Once failure occurs, it will greatly affect the production safety and efficiency of mechanical equipment. Therefore, it is very important to monitor and diagnose the damage state of rolling bearing. It is very difficult to monitor the damage state of low speed heavy load bearing because of its complex structure and operation. With the rapid development of machinery manufacturing industry, the practical application of low-speed and heavy-duty bearings is becoming more and more extensive, and this kind of bearings are generally installed in large and medium-sized rotating machinery equipment. The maintenance and replacement need a lot of manpower and financial resources, so monitoring the damage state of this kind of bearing in advance can avoid the downtime accident and obtain greater economic benefit. Acoustic emission technology (AE) is a new dynamic real-time monitoring technology. Compared with traditional detection technology, acoustic emission signal is sensitive to dynamic defects, wide frequency band and high detection efficiency. The early damage of low speed heavy load rolling bearing can be found in time. It has important engineering application value for the maintenance and repair of bearing in rotating machinery and equipment. In this paper, the acoustic emission testing technology is used to simulate the running state of low-speed and heavy-load bearing by building an experimental bench. The acoustic emission signals of bearings with different damage states are collected for the artificial defects in different positions and sizes of prefabricated bearings. The feasibility of fault diagnosis and damage monitoring of low speed bearing is studied theoretically and experimentally. The main work and achievements are as follows: the acoustic emission signals of bearings with different damage states are collected by means of the test bed, the signals are analyzed and processed by wavelet analysis and wavelet packet analysis, and the percentage of energy occupied by each frequency band is extracted. Compared with wavelet analysis, wavelet packet analysis can extract the main frequency band of acoustic emission signal of bearing fault, and the frequency band with higher energy is consistent with the high amplitude frequency band of spectrum diagram. The feature extraction performance of wavelet scale spectrum and STFT spectrum for low speed bearing AE signal is compared. The results show that wavelet scale spectrum has higher time resolution for non-stationary acoustic emission signal. Compared with wavelet scale spectrum, STFT spectrum has higher frequency resolution for non-stationary signal, so wavelet scale spectrum and STFT spectrum can be combined to extract fault feature of low-speed bearing. Since the fault characteristics of low speed bearing are weak and easily submerged by noise, a low speed bearing fault diagnosis based on energy entropy and set empirical mode decomposition (EEMD) is proposed. Based on the correlation coefficient method and the variance contribution rate method, the effective intrinsic modal components are selected. The experimental results show that the effective IMF component can be selected by using the cross-correlation number and variance contribution rate, and the extracted effective IMF energy entropy can well characterize the damage and defect change of low-speed bearing. The classification and recognition effects of support vector machine and BP neural network on low speed bearing fault types are compared. It is concluded that the recognition accuracy of support vector machine for small sample data of low speed bearing is higher than that of BP neural network.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 彭進(jìn);王維慶;王海云;唐新安;;基于EEMD峭度-相關(guān)系數(shù)準(zhǔn)則的多特征量風(fēng)電機(jī)組軸承故障診斷[J];可再生能源;2016年10期

2 孫永生;李猛;劉恒;景敏卿;王鳳濤;;基于聲發(fā)射檢測(cè)技術(shù)的滾動(dòng)軸承缺陷檢測(cè)[J];無(wú)損檢測(cè);2015年08期

3 郭福平;段志宏;孫志偉;;基于包絡(luò)譜分析的滾動(dòng)軸承滾動(dòng)體故障聲發(fā)射診斷研究[J];組合機(jī)床與自動(dòng)化加工技術(shù);2015年02期

4 艾延廷;馮研研;周海侖;;小波變換和EEMD-馬氏距離的軸承故障診斷[J];噪聲與振動(dòng)控制;2015年01期

5 陳仁祥;湯寶平;呂中亮;;基于相關(guān)系數(shù)的EEMD轉(zhuǎn)子振動(dòng)信號(hào)降噪方法[J];振動(dòng).測(cè)試與診斷;2012年04期

6 胡愛(ài)軍;馬萬(wàn)里;唐貴基;;基于集成經(jīng)驗(yàn)?zāi)B(tài)分解和峭度準(zhǔn)則的滾動(dòng)軸承故障特征提取方法[J];中國(guó)電機(jī)工程學(xué)報(bào);2012年11期

7 于金濤;趙樹延;王祁;;基于經(jīng)驗(yàn)?zāi)B(tài)分解和小波變換聲發(fā)射信號(hào)去噪[J];哈爾濱工業(yè)大學(xué)學(xué)報(bào);2011年10期

8 董文智;張超;;基于EEMD能量熵和支持向量機(jī)的軸承故障診斷[J];機(jī)械設(shè)計(jì)與研究;2011年05期

9 張穎;蘇憲章;劉占生;;基于周期性聲發(fā)射撞擊計(jì)數(shù)的滾動(dòng)軸承故障診斷[J];軸承;2011年06期

10 崔玲麗;康晨暉;胥永剛;高立新;;滾動(dòng)軸承早期沖擊性故障特征提取的綜合算法研究[J];儀器儀表學(xué)報(bào);2010年11期

相關(guān)博士學(xué)位論文 前1條

1 劉國(guó)華;聲發(fā)射信號(hào)處理關(guān)鍵技術(shù)研究[D];浙江大學(xué);2008年

相關(guān)碩士學(xué)位論文 前6條

1 王德麗;基于改進(jìn)HHT與SVM的滾動(dòng)軸承故障診斷方法研究[D];北京交通大學(xué);2016年

2 牛家驊;基于EEMD和SVM聯(lián)合診斷的發(fā)動(dòng)機(jī)故障分析[D];內(nèi)蒙古工業(yè)大學(xué);2015年

3 劉浩;基于聲發(fā)射技術(shù)的貨車滾動(dòng)軸承故障診斷研究[D];中南大學(xué);2010年

4 廖傳軍;基于聲發(fā)射技術(shù)的滾動(dòng)軸承故障診斷時(shí)頻分析方法研究[D];湖南科技大學(xué);2008年

5 印欣運(yùn);聲發(fā)射技術(shù)在旋轉(zhuǎn)機(jī)械碰摩故障診斷中的應(yīng)用[D];清華大學(xué);2005年

6 卜楠楠;基于應(yīng)力波與小波分析的低速滾動(dòng)軸承故障診斷研究[D];沈陽(yáng)工業(yè)大學(xué);2005年

，

本文編號(hào)：2374363