【摘要】：旋轉(zhuǎn)機(jī)械在現(xiàn)代化機(jī)械設(shè)備占很大的比重,為其進(jìn)行狀態(tài)監(jiān)測和故障診斷已經(jīng)成為重要的研究課題。在故障診斷中,最關(guān)鍵的問題是提取故障特征信息和故障類型識別部分。局部均值分解(Local Mean Decomposition,LMD)時頻分析方法在分析機(jī)械振動信號時擁有很多優(yōu)越之處,被廣泛應(yīng)用到旋轉(zhuǎn)機(jī)械故障特征提取當(dāng)中。然而,局部均值分解仍存在一些不足之處有待改進(jìn)。本文重點(diǎn)研究了LMD時頻分析方法的不足之處及改進(jìn)辦法,并研究了故障類型的模式識別方法和故障診斷系統(tǒng)的開發(fā)應(yīng)用。首先,針對LMD存在的端點(diǎn)效應(yīng)問題,分析其產(chǎn)生原因,并提出一種改進(jìn)的方法——最大相似系數(shù)法,通過仿真和實驗研究的對比分析,驗證方法的有效性。其次,針對進(jìn)行旋轉(zhuǎn)機(jī)械故障特征提取時存在的微弱高頻信號難以提取的問題,以及LMD分解結(jié)果存在的虛假頻率問題,提出基于微分局部均值分解(Differential Local Mean Decomposition,DLMD)的故障診斷方法。采用仿真研究,驗證該方法的可行性和有效性。并通過實際工程中的復(fù)合故障信號進(jìn)行研究分析,驗證該方法在實際應(yīng)用中的可行性。然后,針對旋轉(zhuǎn)機(jī)械故障類型的模式識別方面,將LMD方法與樣本熵、模糊聚類結(jié)合,提出基于局部均值分解、樣本熵和模糊聚類的旋轉(zhuǎn)機(jī)械故障診斷方法。該方法首先對旋轉(zhuǎn)機(jī)械振動信號進(jìn)行LMD分解,分解得到的乘積函數(shù)(Product Function,PF)求取樣本熵,以此作為特征向量來建立模糊矩陣,進(jìn)行模糊聚類分析和模式識別,實現(xiàn)故障的分類和診斷。最后,結(jié)合MATLAB和Lab VIEW開發(fā)旋轉(zhuǎn)機(jī)械故障診斷平臺,應(yīng)用Lab VIEW圖形化編程語言的優(yōu)勢和MATLAB強(qiáng)大的數(shù)據(jù)處理能力,進(jìn)行機(jī)械故障診斷界面的設(shè)計和故障數(shù)據(jù)的處理。
[Abstract]:Rotating machinery accounts for a large proportion of modern machinery and equipment, so it has become an important research topic to carry out condition monitoring and fault diagnosis for it. In fault diagnosis, the key problem is to extract fault feature information and fault type identification. Local mean decomposition (Local Mean Decomposition,LMD) time-frequency analysis method has many advantages in the analysis of mechanical vibration signals, and is widely used in fault feature extraction of rotating machinery. However, there are still some shortcomings in local mean decomposition that need to be improved. In this paper, the shortcomings and improvement of LMD time-frequency analysis method are studied, and the pattern recognition method of fault type and the development and application of fault diagnosis system are studied. Firstly, aiming at the problem of endpoint effect in LMD, the causes are analyzed, and an improved method, the maximum similarity coefficient method, is proposed to verify the effectiveness of the method through the comparative analysis of simulation and experimental research. Secondly, in order to solve the problem that it is difficult to extract weak high frequency signals in rotating machinery fault feature extraction, and the false frequency problem of LMD decomposition results, a differential local mean decomposition (Differential Local Mean Decomposition, is proposed. DLMD) fault diagnosis method. The feasibility and effectiveness of the method are verified by simulation. Through the research and analysis of the compound fault signal in practical engineering, the feasibility of this method in practical application is verified. Then, aiming at the pattern recognition of rotating machinery fault types, the LMD method is combined with sample entropy and fuzzy clustering, and a rotating machinery fault diagnosis method based on local mean decomposition, sample entropy and fuzzy clustering is proposed. In this method, the vibration signal of rotating machinery is decomposed by LMD, and the sample entropy is obtained by decomposing the product function (Product Function,PF), which is used as the eigenvector to establish the fuzzy matrix, and the fuzzy clustering analysis and pattern recognition are carried out. Realize the classification and diagnosis of faults. Finally, combined with MATLAB and Lab VIEW, the fault diagnosis platform of rotating machinery is developed, and the design of mechanical fault diagnosis interface and the processing of fault data are carried out by using the advantages of Lab VIEW graphical programming language and the powerful data processing ability of MATLAB.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 徐春林,江志農(nóng),肖英;基于Hilbert變換的包絡(luò)分析及其在機(jī)械故障診斷中的應(yīng)用[J];機(jī)電工程技術(shù);2004年07期

2 何成兵,顧煜炯,楊昆;一種新的轉(zhuǎn)子碰摩故障診斷特征的研究[J];機(jī)械強(qiáng)度;2003年04期

3 ;Practical implementation of Hilbert-Huang Transform algorithm[J];Acta Oceanologica Sinica;2003年01期

4 曾新紅;林春熙;蘇一丹;;基于LabVIEW與MATLAB的電機(jī)監(jiān)測與故障診斷[J];機(jī)械工程與自動化;2014年04期

5 佟俐;潘宏俠;胡田;;基于LabVIEW的機(jī)電設(shè)備狀態(tài)監(jiān)測與故障診斷系統(tǒng)[J];儀表技術(shù)與傳感器;2008年07期

6 陳鐵華,陳啟卷;模糊聚類分析在水電機(jī)組振動故障診斷中的應(yīng)用[J];中國電機(jī)工程學(xué)報;2002年03期

相關(guān)博士學(xué)位論文 前1條

1 鞠萍華;旋轉(zhuǎn)機(jī)械早期故障特征提取的時頻分析方法研究[D];重慶大學(xué);2010年

相關(guān)碩士學(xué)位論文 前7條

1 陳平;信號瞬時頻率的估計方法及其應(yīng)用[D];山東大學(xué);2007年

2 譚宇碩;基于改進(jìn)HHT方法的旋轉(zhuǎn)機(jī)械故障診斷的研究[D];華北電力大學(xué)（河北）;2007年

3 高二山;基于現(xiàn)代信號分析方法的滾動軸承故障診斷的研究[D];華北電力大學(xué)（北京）;2009年

4 羊初發(fā);基于EMD的時頻分析與濾波研究[D];電子科技大學(xué);2009年

5 田佳;基于模糊的設(shè)計模式挖掘與重構(gòu)[D];大連理工大學(xué);2009年

6 許哲;局部均值分解在信號處理中的應(yīng)用[D];西安電子科技大學(xué);2013年

7 李姍姍;基于LMD時頻分析的旋轉(zhuǎn)機(jī)械故障特征提取方法研究[D];燕山大學(xué);2013年

，

本文編號：2484844