本文選題：齒輪箱故障診斷 + 經(jīng)驗(yàn)?zāi)B(tài)分解��； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：齒輪箱常常運(yùn)行在惡劣地環(huán)境下,在運(yùn)行過程中很容易出現(xiàn)故障。齒輪箱一旦出現(xiàn)故障,輕則引起生產(chǎn)中斷與經(jīng)濟(jì)損失,重則會(huì)導(dǎo)致嚴(yán)重人員傷亡。振動(dòng)信號(hào)分析法是齒輪箱故障診斷最重要方法之一。在正常狀態(tài)運(yùn)行時(shí),齒輪箱的振動(dòng)信號(hào)一般表現(xiàn)為平穩(wěn)性的特點(diǎn),而當(dāng)出現(xiàn)故障時(shí),其振動(dòng)信號(hào)通常表現(xiàn)為非線性與非平穩(wěn)的形式。因此,需要使用時(shí)頻分析法分析其振動(dòng)信號(hào)。傳統(tǒng)的時(shí)頻分析法雖然能用于分析處理齒輪故障振動(dòng)信號(hào),但是這些方法都存在較大的局限性—非自適應(yīng)性。自適應(yīng)時(shí)頻分析法能夠滿足自適應(yīng)的要求。經(jīng)驗(yàn)?zāi)B(tài)分解法、局部均值分解和極值點(diǎn)對(duì)稱模態(tài)分解法是目前最主要的自適應(yīng)時(shí)頻分析法。本論文研究了主流自適應(yīng)時(shí)頻分析方法在故障診斷中的應(yīng)用,并對(duì)它們存在的問題提出相應(yīng)的改進(jìn),并將其用于齒輪箱中常用的零件的故障診斷。本論文的主要研究工作有:(1)介紹了齒輪箱主要零件齒輪的失效的形式及原因、振動(dòng)機(jī)理以及故障時(shí)振動(dòng)信號(hào)的模型。(2)介紹了經(jīng)驗(yàn)?zāi)B(tài)分解法的原理及存在的主要的缺陷。針對(duì)經(jīng)驗(yàn)?zāi)B(tài)分解法現(xiàn)有篩分準(zhǔn)則存在的問題,提出了一種型號(hào)篩分終止準(zhǔn)則。存在針對(duì)經(jīng)驗(yàn)?zāi)B(tài)分解存在的模態(tài)混淆現(xiàn)象這一缺陷,提出了解析經(jīng)驗(yàn)?zāi)B(tài)分解法。對(duì)仿真信號(hào)進(jìn)行了分解,驗(yàn)證了該方法的有效性。(3)介紹了局部均值分解法的原理。針對(duì)局部分解存在的模態(tài)混淆現(xiàn)象,提出了小波局部分解法。采用小波局部分解法與總體平均局部均值法分別對(duì)仿真信號(hào)與轉(zhuǎn)子碰摩故障信號(hào)進(jìn)行分解,最終結(jié)果表明,本文提出的小波局部分解方法能夠用于齒輪箱的轉(zhuǎn)軸故障診斷。而且,與總體平均局部均值法相比,該方法具有運(yùn)行效率較高,分解的時(shí)間更短,信號(hào)分解準(zhǔn)確性更好等優(yōu)點(diǎn)。(4)極值點(diǎn)對(duì)稱模態(tài)分解是一種新的自適應(yīng)信號(hào)時(shí)頻分析法,該方法目前尚未應(yīng)用于機(jī)械故障診斷中。本論文將極值點(diǎn)對(duì)稱模態(tài)分解法與能量算子解調(diào)法結(jié)合起來,用于分析齒輪斷齒故障的振動(dòng)信號(hào),從而實(shí)施對(duì)齒輪斷齒故障的診斷。
[Abstract]:The gearbox often runs in bad environment, and it is easy to break down during operation. Once the gearbox fails, the light will cause the production interruption and economic loss, and the heavy will lead to serious casualties. Vibration signal analysis is one of the most important methods for gearbox fault diagnosis. In normal operation, the vibration signal of the gearbox usually shows the characteristics of stationarity, but when the fault occurs, the vibration signal of the gearbox usually shows the form of nonlinearity and non-stationarity. Therefore, it is necessary to use time-frequency analysis method to analyze its vibration signal. Although the traditional time-frequency analysis method can be used to analyze and process the vibration signal of gear fault, these methods have great limitation-non-adaptive. Adaptive time-frequency analysis can meet the requirements of adaptive. The empirical mode decomposition method, the local mean decomposition method and the extreme point symmetric mode decomposition method are the most important adaptive time-frequency analysis methods. In this paper, the application of mainstream adaptive time-frequency analysis method in fault diagnosis is studied, and the corresponding improvement of their existing problems is put forward, and applied to the fault diagnosis of common parts in gearbox. The main research work of this paper is: (1) introducing the form and reason of gear failure, vibration mechanism and vibration signal model. (2) introducing the principle of empirical mode decomposition method and its main defects. In order to solve the problems existing in the existing screening criteria of empirical mode decomposition (EMD), a model sieving termination criterion is proposed. In order to solve the problem of modal confusion in EMD, an analytical empirical mode decomposition (EMD) method is proposed. The simulation signal is decomposed and the validity of the method is verified. The principle of the local mean decomposition method is introduced. A wavelet local decomposition method is proposed to solve the modal confusion in local decomposition. The wavelet local decomposition method and the population average local mean method are used to decompose the simulated signal and the rotor rubbing fault signal respectively. The final results show that the wavelet partial decomposition method proposed in this paper can be used to diagnose the rotating shaft fault of the gearbox. In addition, compared with the average local mean method, this method has the advantages of higher running efficiency, shorter decomposition time, better signal decomposition accuracy, etc.) extreme point symmetric mode decomposition is a new adaptive signal time-frequency analysis method. At present, this method has not been applied to mechanical fault diagnosis. In this paper, the extreme point symmetric mode decomposition method and the energy operator demodulation method are combined to analyze the vibration signal of gear broken tooth fault, so as to carry out the diagnosis of gear broken tooth fault.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH132.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孟宗;王亞超;;基于微分局部均值分解的旋轉(zhuǎn)機(jī)械故障診斷方法[J];機(jī)械工程學(xué)報(bào);2014年11期

2 雷亞國;孔德同;李乃鵬;林京;;自適應(yīng)總體平均經(jīng)驗(yàn)?zāi)Ｊ椒纸饧捌湓谛行驱X輪箱故障檢測(cè)中的應(yīng)用[J];機(jī)械工程學(xué)報(bào);2014年03期

3 徐繼剛;趙榮珍;朱永生;于昊;;局部均值分解在旋轉(zhuǎn)機(jī)械復(fù)合故障診斷中的應(yīng)用[J];噪聲與振動(dòng)控制;2012年05期

4 許雪貴;徐文琴;;齒輪箱故障的振動(dòng)機(jī)理與故障特征研究[J];機(jī)械制造與自動(dòng)化;2012年04期

5 張亢;程軍圣;楊宇;;基于局部均值分解的階次跟蹤分析及其在齒輪故障診斷中的應(yīng)用[J];中國機(jī)械工程;2011年14期

6 程軍圣;楊怡;張亢;楊宇;;基于局部均值分解的循環(huán)頻率和能量譜在齒輪故障診斷中的應(yīng)用[J];振動(dòng)工程學(xué)報(bào);2011年01期

7 王冬;;基于小波系數(shù)區(qū)域相關(guān)性的電能質(zhì)量擾動(dòng)檢測(cè)[J];中國產(chǎn)業(yè);2011年02期

8 劉暢;周川;伍星;遲毅林;;基于廣義形態(tài)濾波和相關(guān)系數(shù)的Hilbert-Huang變換方法[J];機(jī)械科學(xué)與技術(shù);2011年01期

9 湯寶平;蔣永華;張?jiān)敶?;基于形態(tài)奇異值分解和經(jīng)驗(yàn)?zāi)B(tài)分解的滾動(dòng)軸承故障特征提取方法[J];機(jī)械工程學(xué)報(bào);2010年05期

10 胡勁松;楊世錫;;基于能量的振動(dòng)信號(hào)經(jīng)驗(yàn)?zāi)B(tài)分解終止條件[J];振動(dòng)、測(cè)試與診斷;2009年01期

相關(guān)博士學(xué)位論文 前7條

1 李蓉;齒輪箱復(fù)合故障診斷方法研究[D];湖南大學(xué);2013年

2 張超;基于自適應(yīng)振動(dòng)信號(hào)處理的旋轉(zhuǎn)機(jī)械故障診斷研究[D];西安電子科技大學(xué);2012年

3 張亢;局部均值分解方法及其在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用研究[D];湖南大學(xué);2012年

4 呂蓬;旋轉(zhuǎn)機(jī)械故障模式識(shí)別方法研究[D];華北電力大學(xué)（北京）;2010年

5 任達(dá)千;基于局域均值分解的旋轉(zhuǎn)機(jī)械故障特征提取方法及系統(tǒng)研究[D];浙江大學(xué);2008年

6 王春;基于小波和分形理論的齒輪故障特征提取及噪聲的和諧化研究[D];重慶大學(xué);2006年

7 程軍圣;基于Hilbert-Huang變換的旋轉(zhuǎn)機(jī)械故障診斷方法研究[D];湖南大學(xué);2005年

相關(guān)碩士學(xué)位論文 前3條

1 張凡;基于核密度估計(jì)和K-L散度的旋轉(zhuǎn)機(jī)械故障診斷與健康評(píng)估方法研究[D];電子科技大學(xué);2015年

2 楊怡;基于局部均值分解的齒輪故障診斷方法[D];湖南大學(xué);2012年

3 余磊;Hilbert-Huang變換及其在故障檢測(cè)中的應(yīng)用[D];武漢理工大學(xué);2009年

，

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