聲發(fā)射技術(shù)在超低速軸承故障診斷中的應(yīng)用研究
發(fā)布時間：2018-12-12 09:55
【摘要】：滾動軸承作為旋轉(zhuǎn)機械設(shè)備中最為常用的關(guān)鍵零部件之一,其運轉(zhuǎn)狀況往往直接關(guān)系到整臺設(shè)備的安全穩(wěn)定運行,一旦產(chǎn)生故障,將會極大的影響機械設(shè)備的生產(chǎn)安全和效率。因此,對滾動軸承的損傷狀態(tài)進行監(jiān)測診斷就顯得尤為重要。低速重載軸承由于其運轉(zhuǎn)和本身結(jié)構(gòu)的復(fù)雜特殊,對于這類軸承的損傷狀態(tài)進行監(jiān)測異常困難。隨著機械制造行業(yè)的快速發(fā)展,低速重載軸承的實際應(yīng)用范疇也越來越廣泛,并且這類軸承一般都安裝在大中型的旋轉(zhuǎn)機械設(shè)備中,一旦產(chǎn)生損壞造成停機,其維修更換需要大量的人力物力財力,因此提前監(jiān)測這類軸承的損傷狀態(tài)能夠避免停機事故,獲得較大的經(jīng)濟效益。聲發(fā)射技術(shù)(AE)是一種新型的動態(tài)實時監(jiān)測技術(shù),與傳統(tǒng)的檢測技術(shù)相比,聲發(fā)射信號對動態(tài)缺陷敏感、頻帶較寬,檢測效率高,可以及時的發(fā)現(xiàn)低速重載滾動軸承的早期損傷,對于旋轉(zhuǎn)機械設(shè)備中軸承的保養(yǎng)和維修具有重要的工程應(yīng)用價值。本文以聲發(fā)射檢測技術(shù)為手段,通過搭建實驗臺來模擬低速重載軸承的運行狀態(tài),對軸承預(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,對其在低速軸承故障診斷和損傷狀態(tài)監(jiān)測的可行性進行了理論和實驗研究。完成的主要工作和成果有:借助試驗臺采集不同損傷狀態(tài)的軸承聲發(fā)射信號,分別采用小波分析和小波包分析對信號進行分析處理,通過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譜對低速軸承AE信號中的特征提取性能,結(jié)果表明小波尺度譜對于非平穩(wěn)聲發(fā)射信號的時間分辨率較高,而STFT譜相比小波尺度譜對于非平穩(wěn)信號的頻率分辨率較高,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。針對低速軸承故障特征微弱,易被噪聲淹沒,提出了結(jié)合能量熵和集合經(jīng)驗?zāi)B(tài)分解(EEMD)進行低速軸承故障診斷,并提出基于相關(guān)系數(shù)法和方差貢獻率法篩選有效本征模態(tài)分量。通過實驗結(jié)果表明,采用互相關(guān)系數(shù)和方差貢獻率能夠篩選有效的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。并對比了支持向量機和BP神經(jīng)網(wǎng)絡(luò)對低速軸承的故障類型的分類識別效果,得出針對低速軸承小樣本數(shù)據(jù)支持向量機的識別準確率要高于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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.3
本文編號：2374363
[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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH133.3
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