基于變分模態(tài)分解的旋轉(zhuǎn)機械故障診斷研究
發(fā)布時間：2018-09-04 16:19
【摘要】：轉(zhuǎn)子、滾動軸承等是工業(yè)生產(chǎn)機械中許多機器設(shè)備的重要零部件,對機器的正常運轉(zhuǎn)有重要影響。故障振動信號一般具有非平穩(wěn)、非線性、非高斯等特性,單一的方法難以提取故障振動信號中有效的特征信息。因此本文采用變分模態(tài)分解(Variational Mode Decomposition,VMD)這種全新的信號處理方法,并結(jié)合一些其他信號處理手段,對機械故障振動信號進行分析處理。主要研究內(nèi)容如下:1、基于變分模態(tài)分解的轉(zhuǎn)子故障時頻分析方法針對轉(zhuǎn)子故障診斷問題,使用一種基于變分模態(tài)分解的信號處理方法,該方法在獲取分解分量的過程中通過迭代搜尋變分模型最優(yōu)解來確定每個分量的頻率中心及帶寬,從而能夠自適應(yīng)地實現(xiàn)信號的頻域剖分及各分量的有效分離,對各單分量信號進行希爾伯特變換即可得到瞬時頻率和幅值信息。針對仿真信號和典型轉(zhuǎn)子故障信號進行VMD方法和EMD方法的分析比較,以驗證所提方法的有效性。仿真信號的分解結(jié)果表明,變分模態(tài)能夠準確分離出信號中的固有模態(tài)分量且不存在模態(tài)混疊;轉(zhuǎn)子故障實驗信號的分析結(jié)果表明,所提方法能夠有效提取出明顯的故障特征,從而準確診斷出轉(zhuǎn)子存在的故障。2、基于VMD和1.5維Teager能量譜的滾動軸承故障特征提取為準確提取滾動軸承故障信號中的故障特征,使用基于VMD和1.5維Teager能量譜的滾動軸承故障特征提取方法。故障特征提取過程:首先,對滾動軸承故障信號進行VMD分解得到一組分量,根據(jù)峭度-相關(guān)系數(shù)準則篩選分量進行信號重構(gòu);再次,對重構(gòu)信號進行1.5維Teager能量譜分析,根據(jù)能量譜圖的分析,提取出滾動軸承的內(nèi)圈和滾動體故障特征。仿真和實驗信號的分析驗證了所提方法的有效性。與EEMD比較,采用VMD和1.5維Teager能量譜的分析方法更具有區(qū)分性,可以有效識別滾動軸承的故障特征。3、基于VMD、模糊熵和模糊C均值聚類的滾動軸承故障診斷使用一種基于VMD、模糊熵和模糊C均值聚類(FCM)算法的模式識別方法。首先采用VMD方法對信號進行分解,取相關(guān)性較大的分量組成初始特征向量矩陣;而后對初始特征向量矩陣求取模糊熵值,組成模糊熵值特征向量矩陣;最后將模糊熵值特征向量矩陣作為數(shù)據(jù)源輸入FCM進行故障模式識別。將該方法應(yīng)用于滾動軸承的故障模式識別,并與基于EMD和FCM的模式識別方法進行對比,驗證了所提方法的有效性。
[Abstract]:Rotor, rolling bearing and so on are important parts of many machines and equipments in industrial production machinery, which have an important effect on the normal operation of the machine. The fault vibration signal usually has the characteristics of non-stationary, nonlinear and non-Gao Si, so it is difficult to extract the effective characteristic information from the fault vibration signal by a single method. Therefore, this paper adopts variational mode decomposition (Variational Mode Decomposition,VMD), a new signal processing method, and combines some other signal processing methods to analyze and process the vibration signals of mechanical faults. The main research contents are as follows: 1. The rotor fault time-frequency analysis method based on variational mode decomposition is used to solve the rotor fault diagnosis problem, and a signal processing method based on variational mode decomposition is used. In the process of obtaining decomposed components, the frequency center and bandwidth of each component can be determined by iterative search for the optimal solution of the variational model, so that the frequency domain partition and the effective separation of each component can be realized adaptively. The instantaneous frequency and amplitude information can be obtained by Hilbert transform for each single component signal. In order to verify the effectiveness of the proposed method, the VMD method and the EMD method are compared between the simulated signal and the typical rotor fault signal. The decomposition results of simulation signals show that the inherent modal components in the signals can be separated accurately and there is no modal aliasing, and the analysis results of the rotor fault signals show that the proposed method can extract the obvious fault characteristics effectively. The fault features of rolling bearings based on VMD and 1.5-D Teager energy spectrum are extracted to accurately extract fault features from fault signals of rolling bearings. The fault feature extraction method of rolling bearing based on VMD and 1.5 D Teager energy spectrum is used. Fault feature extraction process: first, the rolling bearing fault signal is decomposed into a group of components by VMD decomposition, and the signal is reconstructed according to the kurtosis correlation coefficient criterion. Thirdly, the 1.5 dimensional Teager energy spectrum analysis of the reconstructed signal is carried out. According to the analysis of energy spectrum, the fault characteristics of inner ring and rolling body of rolling bearing are extracted. The effectiveness of the proposed method is verified by simulation and experimental signal analysis. Compared with EEMD, VMD and 1.5-dimensional Teager energy spectrum analysis methods are more discriminative. Rolling bearing fault diagnosis based on VMD, fuzzy entropy and fuzzy C-means clustering uses a pattern recognition method based on VMD, fuzzy entropy and fuzzy C-means clustering (FCM) algorithm. Firstly, the signal is decomposed by VMD method, and the components with high correlation are used to form the initial eigenvector matrix, then the fuzzy entropy value is obtained for the initial eigenvector matrix, and the fuzzy entropy eigenvector matrix is formed. Finally, the fuzzy entropy eigenvector matrix is used as the input of FCM for fault pattern recognition. The method is applied to the fault pattern recognition of rolling bearing, and compared with the pattern recognition method based on EMD and FCM, the effectiveness of the proposed method is verified.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH17
本文編號：2222700
[Abstract]:Rotor, rolling bearing and so on are important parts of many machines and equipments in industrial production machinery, which have an important effect on the normal operation of the machine. The fault vibration signal usually has the characteristics of non-stationary, nonlinear and non-Gao Si, so it is difficult to extract the effective characteristic information from the fault vibration signal by a single method. Therefore, this paper adopts variational mode decomposition (Variational Mode Decomposition,VMD), a new signal processing method, and combines some other signal processing methods to analyze and process the vibration signals of mechanical faults. The main research contents are as follows: 1. The rotor fault time-frequency analysis method based on variational mode decomposition is used to solve the rotor fault diagnosis problem, and a signal processing method based on variational mode decomposition is used. In the process of obtaining decomposed components, the frequency center and bandwidth of each component can be determined by iterative search for the optimal solution of the variational model, so that the frequency domain partition and the effective separation of each component can be realized adaptively. The instantaneous frequency and amplitude information can be obtained by Hilbert transform for each single component signal. In order to verify the effectiveness of the proposed method, the VMD method and the EMD method are compared between the simulated signal and the typical rotor fault signal. The decomposition results of simulation signals show that the inherent modal components in the signals can be separated accurately and there is no modal aliasing, and the analysis results of the rotor fault signals show that the proposed method can extract the obvious fault characteristics effectively. The fault features of rolling bearings based on VMD and 1.5-D Teager energy spectrum are extracted to accurately extract fault features from fault signals of rolling bearings. The fault feature extraction method of rolling bearing based on VMD and 1.5 D Teager energy spectrum is used. Fault feature extraction process: first, the rolling bearing fault signal is decomposed into a group of components by VMD decomposition, and the signal is reconstructed according to the kurtosis correlation coefficient criterion. Thirdly, the 1.5 dimensional Teager energy spectrum analysis of the reconstructed signal is carried out. According to the analysis of energy spectrum, the fault characteristics of inner ring and rolling body of rolling bearing are extracted. The effectiveness of the proposed method is verified by simulation and experimental signal analysis. Compared with EEMD, VMD and 1.5-dimensional Teager energy spectrum analysis methods are more discriminative. Rolling bearing fault diagnosis based on VMD, fuzzy entropy and fuzzy C-means clustering uses a pattern recognition method based on VMD, fuzzy entropy and fuzzy C-means clustering (FCM) algorithm. Firstly, the signal is decomposed by VMD method, and the components with high correlation are used to form the initial eigenvector matrix, then the fuzzy entropy value is obtained for the initial eigenvector matrix, and the fuzzy entropy eigenvector matrix is formed. Finally, the fuzzy entropy eigenvector matrix is used as the input of FCM for fault pattern recognition. The method is applied to the fault pattern recognition of rolling bearing, and compared with the pattern recognition method based on EMD and FCM, the effectiveness of the proposed method is verified.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH17
【參考文獻】
相關(guān)期刊論文 前10條
1 唐貴基;王曉龍;;IVMD融合奇異值差分譜的滾動軸承早期故障診斷[J];振動.測試與診斷;2016年04期
2 姚家馳;向陽;李勝楊;王帥;;基于VMD-ICA-CWT的內(nèi)燃機噪聲源識別方法[J];華中科技大學學報(自然科學版);2016年07期
3 馬增強;李亞超;劉政;谷朝健;;基于變分模態(tài)分解和Teager能量算子的滾動軸承故障特征提取[J];振動與沖擊;2016年13期
4 曹瑩;段玉波;劉繼承;;Hilbert-Huang變換中的模態(tài)混疊問題[J];振動.測試與診斷;2016年03期
5 王志搏;李富才;孟立立;張希;;基于ITD和模糊熵的滾動軸承智能診斷[J];噪聲與振動控制;2016年01期
6 向玲;李媛媛;;經(jīng)驗小波變換在旋轉(zhuǎn)機械故障診斷中的應(yīng)用[J];動力工程學報;2015年12期
7 李學軍;何能勝;何寬芳;何雷;;基于小波包近似熵和SVM的圓柱滾子軸承診斷[J];振動.測試與診斷;2015年06期
8 張亢;程軍圣;;基于局部均值分解和峭度圖的滾動軸承包絡(luò)分析方法[J];航空動力學報;2015年12期
9 張淑清;胡永濤;李盼;包紅燕;姜萬錄;錢磊;;基于MEMD互近似熵及FCM聚類的軸承故障診斷方法[J];中國機械工程;2015年19期
10 馬文朋;張俊紅;馬梁;劉昱;賈曉杰;;改進的經(jīng)驗?zāi)Ｊ椒纸庠跈C械故障診斷中的應(yīng)用[J];振動.測試與診斷;2015年04期
相關(guān)博士學位論文 前2條
1 胡愛軍;Hilbert-Huang變換在旋轉(zhuǎn)機械振動信號分析中的應(yīng)用研究[D];華北電力大學（河北）;2008年
2 程軍圣;基于Hilbert-Huang變換的旋轉(zhuǎn)機械故障診斷方法研究[D];湖南大學;2005年
相關(guān)碩士學位論文 前2條
1 王振威;基于變分模態(tài)分解的故障診斷方法研究[D];燕山大學;2015年
2 顧小軍;面向旋轉(zhuǎn)機械的支持向量機方法及智能故障診斷系統(tǒng)研究[D];浙江大學;2006年
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