本文選題：齒輪箱　切入點(diǎn)：EMD　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在旋轉(zhuǎn)機(jī)械中,齒輪箱是傳遞動力的主要部件,齒輪箱中的旋轉(zhuǎn)部件如軸、軸承、齒輪等往往在復(fù)雜的變載荷環(huán)境下運(yùn)行,這些部件因為疲勞損傷而產(chǎn)生微弱故障,而故障信號在強(qiáng)背景噪聲下往往不易被察覺。如果任由微弱故障發(fā)展為嚴(yán)重故障,會造成嚴(yán)重的后果。因此選用恰當(dāng)?shù)慕翟敕椒▽υ缙诠收咸卣魈崛∫饬x重大。目前對于機(jī)械信號的處理多是忽略機(jī)械設(shè)備發(fā)生故障時所產(chǎn)生的非高斯非平穩(wěn)振動信號,而傳統(tǒng)的故障診斷方法又無法抑制高斯噪聲對故障信號的影響,往往無法在故障初期提取到具有非平穩(wěn)非高斯特性的微弱故障特征。針對齒輪箱的早期故障信號具有非線性、非平穩(wěn)性、非高斯性且易受強(qiáng)背景噪聲干擾的特點(diǎn),本文提出了基于最小熵反褶積(MED)、經(jīng)驗?zāi)B(tài)分解(EMD)和切片雙譜相結(jié)合的方法來提取微弱故障特征。主要研究內(nèi)容如下:1)介紹了MED、EMD、互信息和切片雙譜的各自的原理以及之間的聯(lián)系和銜接,在此基礎(chǔ)上運(yùn)用MED-EMD切片雙譜的方法分析了的仿真信號,對仿真信號經(jīng)MED-EMD分解后的前三階IMF分量進(jìn)行了切片雙譜分析后發(fā)現(xiàn),在中低頻段出現(xiàn)了載波頻率1)1=70Hz,明顯突出了調(diào)制頻率1)2=200Hz及其邊頻簇、1)2的二倍頻及其邊頻簇和載波頻率1)9)=300Hz的邊頻簇。通過比較得到MED-EMD和切片雙譜相結(jié)合的降噪效果在三種方法中是最優(yōu)的,這驗證了MED-EMD切片雙譜方法應(yīng)用到旋轉(zhuǎn)設(shè)備故障診斷上是可行的。2)通過MED-EMD將原始信號降噪分解為多個本征模態(tài)函數(shù)(IMF),MED作為EMD的前置濾波器能夠彌補(bǔ)強(qiáng)背景噪聲下EMD分解的不足,選取和原始信號相關(guān)性強(qiáng)的IMF分量為有效分量并對其進(jìn)行切片雙譜分析,提取點(diǎn)蝕故障特征。切片雙譜分析能夠抑制高斯噪聲對IMF分量的干擾,同時與傳統(tǒng)的基于EMD分解的功率譜方法進(jìn)行了結(jié)果的對比,驗證了該方法的實用性和優(yōu)越性。3)設(shè)計了具有沖擊調(diào)制特點(diǎn)的仿真信號,通過對仿真信號的處理來完善算法;搭建齒輪箱試驗臺,采集并用文中方法分析了包含齒輪點(diǎn)蝕信息的振動信號,驗證了基于MED-EMD和切片雙譜的方法在應(yīng)用到旋轉(zhuǎn)設(shè)備故障診斷時的實用性;測試采集到的某型號風(fēng)力發(fā)電機(jī)齒輪箱實際運(yùn)行的故障信號,提取微弱故障特征并做出相應(yīng)故障診斷,與開機(jī)檢查的結(jié)果相匹配,得到了齒輪箱故障為中間軸靠電機(jī)側(cè)的軸承內(nèi)圈出現(xiàn)點(diǎn)蝕的正確結(jié)論,并與傳統(tǒng)方法相對比,MED-EMD切片雙譜的降噪明顯提高了信噪比,表明該方法對微弱故障提取的有效性。
[Abstract]:In rotating machinery, gearbox is the main component of transmission power. The rotating parts such as shaft, bearing, gear and so on often run under complicated variable load environment, these parts are weak fault due to fatigue damage. However, the fault signal is often difficult to detect under strong background noise. If a weak fault is allowed to develop into a serious fault, Therefore, it is very important to select proper noise reduction methods for early fault feature extraction. At present, most of the mechanical signal processing is to ignore the non-stationary vibration signal of Gao Si produced when the mechanical equipment is in trouble. However, the traditional fault diagnosis method can not restrain the influence of Gao Si noise on the fault signal, and it is often unable to extract the weak fault characteristics with non-stationary non-#china_person1# characteristics at the early stage of the fault. The early fault signal of the gearbox is nonlinear. Characteristic of non-stationary, non-#china_person0# and easily disturbed by strong background noise, In this paper, a method based on minimum entropy deconvolution (MED), empirical mode decomposition (EMD) and slice bispectrum is proposed to extract weak fault features. The main research contents are as follows: (1) the principles of MED EMD, mutual information and slice bispectrum are introduced. And the connection and convergence between them, On this basis, the simulation signal is analyzed by using MED-EMD slice bispectrum method. The first three IMF components of the simulation signal decomposed by MED-EMD are analyzed by slice bispectrum analysis, and it is found that the first three IMF components of the simulation signal are decomposed by MED-EMD. In the middle and low frequency band, the carrier frequency 1T 1n 70 Hz appears, which highlights the double frequency of modulation frequency 1kW 200Hz and its edge frequency cluster, and the side frequency cluster and the edge frequency cluster of the carrier frequency 1t 9 / 300Hz. By comparison, the noise reduction effect of the combination of MED-EMD and slice bispectral spectrum is obtained. The three methods are optimal. This proves that it is feasible to apply MED-EMD slice bispectral method to the fault diagnosis of rotating equipment. 2) the original signal is decomposed into multiple intrinsic mode functions by MED-EMD. As a prefilter of EMD, it can make up for the deficiency of EMD decomposition under strong background noise. The IMF component with strong correlation with the original signal is selected as the effective component and analyzed by slice bispectrum to extract the pitting fault features. The slice bispectrum analysis can suppress the interference of Gao Si noise to the IMF component. At the same time, compared with the traditional power spectrum method based on EMD decomposition, the practicability and superiority of the method are verified. 3) the simulation signal with the characteristic of impulse modulation is designed, and the algorithm is improved by processing the simulation signal. The vibration signals containing pitting corrosion information of gears are collected and analyzed with the method of gearbox test bed. The practicability of the method based on MED-EMD and slice bispectrum is verified when it is applied to the fault diagnosis of rotating equipment. The fault signals collected from the gearbox of a certain type wind turbine are tested, the weak fault features are extracted and the corresponding fault diagnosis is made, which matches the results of the boot check. The correct conclusion that the inner ring of the bearing on the motor side of the middle shaft of the gearbox fault appears pitting corrosion is obtained. Compared with the traditional method, the noise reduction of the MED-EMD slice bispectrum obviously improves the signal-to-noise ratio (SNR), which shows that the method is effective for weak fault extraction.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TH132.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉尚坤;唐貴基;;自適應(yīng)MED結(jié)合EMD診斷滾動軸承早期故障[J];噪聲與振動控制;2015年06期

2 沈虹;趙紅東;張玲玲;肖云魁;趙慧敏;;基于EMD和Gabor變換的發(fā)動機(jī)曲軸軸承故障特征提取[J];汽車工程;2014年12期

3 周雁冰;柳亦兵;王峰;姜銳;;齒輪故障振動信號非高斯性特征趨勢分析[J];振動與沖擊;2014年06期

4 王宏超;陳進(jìn);董廣明;;基于最小熵解卷積與稀疏分解的滾動軸承微弱故障特征提取[J];機(jī)械工程學(xué)報;2013年01期

5 韓振南;王志堅;;循環(huán)自相關(guān)函數(shù)在風(fēng)電齒輪箱試驗臺振動測試中的應(yīng)用[J];機(jī)械設(shè)計與制造;2012年10期

6 鞏曉峗;韓捷;陳宏;雷文平;;全矢小波包-包絡(luò)分析方法及其在齒輪故障診斷中的應(yīng)用[J];振動與沖擊;2012年12期

7 竇東陽;楊建國;李麗娟;趙英凱;;基于EMD和MLEM2的滾動軸承智能故障診斷方法[J];農(nóng)業(yè)工程學(xué)報;2011年04期

8 張琳;黃敏;;基于EMD與切片雙譜的軸承故障診斷方法[J];北京航空航天大學(xué)學(xué)報;2010年03期

9 趙曉平;張令彌;郭勤濤;;旋轉(zhuǎn)機(jī)械階比跟蹤技術(shù)研究進(jìn)展綜述[J];地震工程與工程振動;2008年06期

10 邵忍平;黃欣娜;劉宏昱;徐永強(qiáng);;基于高階累積量的齒輪系統(tǒng)故障檢測與診斷[J];機(jī)械工程學(xué)報;2008年06期

相關(guān)博士學(xué)位論文 前3條

1 周雁冰;基于高階統(tǒng)計量的齒輪傳動系統(tǒng)故障特征提取方法研究[D];華北電力大學(xué);2013年

2 胡曉依;基于非高斯、非平穩(wěn)信號處理的機(jī)械故障特征提取方法研究[D];北京交通大學(xué);2009年

3 陳仲生;直升機(jī)旋轉(zhuǎn)部件故障特征提取的高階統(tǒng)計量方法研究[D];國防科學(xué)技術(shù)大學(xué);2004年

，

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