【摘要】：轉(zhuǎn)子、滾動(dòng)軸承等是工業(yè)生產(chǎn)機(jī)械中許多機(jī)器設(shè)備的重要零部件,對(duì)機(jī)器的正常運(yùn)轉(zhuǎn)有重要影響。故障振動(dòng)信號(hào)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(dòng)信號(hào)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(hào)處理方法,并結(jié)合一些其他信號(hào)處理手段,對(duì)機(jī)械故障振動(dòng)信號(hào)進(jìn)行分析處理。主要研究?jī)?nèi)容如下:1、基于變分模態(tài)分解的轉(zhuǎn)子故障時(shí)頻分析方法針對(duì)轉(zhuǎn)子故障診斷問(wèn)題,使用一種基于變分模態(tài)分解的信號(hào)處理方法,該方法在獲取分解分量的過(guò)程中通過(guò)迭代搜尋變分模型最優(yōu)解來(lái)確定每個(gè)分量的頻率中心及帶寬,從而能夠自適應(yīng)地實(shí)現(xiàn)信號(hào)的頻域剖分及各分量的有效分離,對(duì)各單分量信號(hào)進(jìn)行希爾伯特變換即可得到瞬時(shí)頻率和幅值信息。針對(duì)仿真信號(hào)和典型轉(zhuǎn)子故障信號(hào)進(jìn)行VMD方法和EMD方法的分析比較,以驗(yàn)證所提方法的有效性。仿真信號(hào)的分解結(jié)果表明,變分模態(tài)能夠準(zhǔn)確分離出信號(hào)中的固有模態(tài)分量且不存在模態(tài)混疊;轉(zhuǎn)子故障實(shí)驗(yàn)信號(hào)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(zhǔn)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(dòng)軸承故障特征提取為準(zhǔn)確提取滾動(dòng)軸承故障信號(hào)中的故障特征,使用基于VMD和1.5維Teager能量譜的滾動(dòng)軸承故障特征提取方法。故障特征提取過(guò)程:首先,對(duì)滾動(dòng)軸承故障信號(hào)進(jìn)行VMD分解得到一組分量,根據(jù)峭度-相關(guān)系數(shù)準(zhǔn)則篩選分量進(jìn)行信號(hào)重構(gòu);再次,對(duì)重構(gòu)信號(hào)進(jìn)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(dòng)軸承的內(nèi)圈和滾動(dòng)體故障特征。仿真和實(shí)驗(yàn)信號(hào)的分析驗(yàn)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(shí)別滾動(dòng)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(dòng)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(shí)別方法。首先采用VMD方法對(duì)信號(hào)進(jìn)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(duì)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(jìn)行故障模式識(shí)別。將該方法應(yīng)用于滾動(dòng)軸承的故障模式識(shí)別,并與基于EMD和FCM的模式識(shí)別方法進(jìn)行對(duì)比,驗(yàn)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(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH17

【參考文獻(xiàn)】

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(dòng)軸承早期故障診斷[J];振動(dòng).測(cè)試與診斷;2016年04期

2 姚家馳;向陽(yáng);李勝楊;王帥;;基于VMD-ICA-CWT的內(nèi)燃機(jī)噪聲源識(shí)別方法[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年07期

3 馬增強(qiáng);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(dòng)軸承故障特征提取[J];振動(dòng)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(wèn)題[J];振動(dòng).測(cè)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(dòng)軸承智能診斷[J];噪聲與振動(dòng)控制;2016年01期

6 向玲;李媛媛;;經(jīng)驗(yàn)小波變換在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用[J];動(dòng)力工程學(xué)報(bào);2015年12期

7 李學(xué)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(dòng).測(cè)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(dòng)軸承包絡(luò)分析方法[J];航空動(dòng)力學(xué)報(bào);2015年12期

9 張淑清;胡永濤;李盼;包紅燕;姜萬(wàn)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(guó)機(jī)械工程;2015年19期

10 馬文朋;張俊紅;馬梁;劉昱;賈曉杰;;改進(jìn)的經(jīng)驗(yàn)?zāi)Ｊ椒纸庠跈C(jī)械故障診斷中的應(yīng)用[J];振動(dòng).測(cè)試與診斷;2015年04期

相關(guān)博士學(xué)位論文 前2條

1 胡愛(ài)軍;Hilbert-Huang變換在旋轉(zhuǎn)機(jī)械振動(dòng)信號(hào)分析中的應(yīng)用研究[D];華北電力大學(xué)（河北）;2008年

2 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(xué);2015年

2 顧小軍;面向旋轉(zhuǎn)機(jī)械的支持向量機(jī)方法及智能故障診斷系統(tǒng)研究[D];浙江大學(xué);2006年

，

本文編號(hào)：2222700