本文關(guān)鍵詞： 經(jīng)驗(yàn)?zāi)B(tài)分解 灰色理論 GM(1 1) 端點(diǎn)效應(yīng) 齒輪故障診斷 希爾伯特一黃變換　出處：《太原理工大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著信號處理技術(shù)的迅速發(fā)展,信號的時(shí)頻分析方法已經(jīng)成為分析處理非線性、非平穩(wěn)信號的重要方法之一。它從信號的時(shí)域和頻域兩個(gè)不同角度來綜合研究信號的特征,能夠同時(shí)了解信號在時(shí)域和頻域的特征信息,是信號處理領(lǐng)域的一個(gè)重大突破。其中經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition,簡稱EMD)是近幾年發(fā)展起來的一種新的時(shí)頻分析方法,由美籍華人N. E.Huang等人于1998年首次提出,它已經(jīng)成為信號時(shí)頻分析的重要途徑之一。 本文簡要介紹了幾種現(xiàn)代時(shí)頻分析方法以及它們的優(yōu)缺點(diǎn),研究了經(jīng)驗(yàn)?zāi)B(tài)分解理論的計(jì)算原理及其存在的不足之處,重點(diǎn)分析了經(jīng)驗(yàn)?zāi)B(tài)分解在處理非線性、非平穩(wěn)信號的“篩分”過程中產(chǎn)生端點(diǎn)效應(yīng)的主要原因,在此基礎(chǔ)上,提出一種新的抑制經(jīng)驗(yàn)?zāi)B(tài)分解端點(diǎn)效應(yīng)的方法,即將離散序列預(yù)測灰色理論GM(1,1)模型應(yīng)用于在經(jīng)驗(yàn)?zāi)B(tài)分解過程中,使得被分解的離散信號序列的端點(diǎn)值向外進(jìn)行適當(dāng)延拓,延拓后產(chǎn)生的新序列很好的反映了原信號的內(nèi)部信息和發(fā)展趨勢,使得在利用三次樣條插值形成的上、下包絡(luò)線時(shí),端點(diǎn)效應(yīng)被抑制而不污染到原始離散序列內(nèi)部,從而可以保證分解出真實(shí)而有效的本征模函數(shù)。此外,齒輪是機(jī)械傳動(dòng)中重要傳動(dòng)部件之一,齒輪故障診斷方法對現(xiàn)代化工業(yè)發(fā)展有著舉足輕重的推動(dòng)作用。當(dāng)齒輪在運(yùn)轉(zhuǎn)中出現(xiàn)有斷齒、磨損和點(diǎn)蝕等故障時(shí),就會引起齒輪強(qiáng)烈的嚙合沖擊振動(dòng),該振動(dòng)信號中包含有周期性故障沖擊振動(dòng)成分,利用改進(jìn)后的經(jīng)驗(yàn)?zāi)B(tài)分解方法從齒輪嚙合振動(dòng)信號中提取齒輪故障特征,為齒輪故障診斷早期預(yù)測和診斷提供了一種更加可靠的方法。 實(shí)驗(yàn)是獲取數(shù)據(jù)和驗(yàn)證理論是否正確的重要途徑。本文數(shù)據(jù)全部來自于齒輪故障的物理模擬實(shí)驗(yàn),該實(shí)驗(yàn)分別采集了齒輪在正常狀態(tài)和故障狀態(tài)下的齒輪嚙合振動(dòng)信號,然后借助于軟件MATLAB對實(shí)驗(yàn)所得數(shù)據(jù)進(jìn)行編程處理。 在試驗(yàn)數(shù)據(jù)基礎(chǔ)上,分別使用改進(jìn)和未改進(jìn)的經(jīng)驗(yàn)?zāi)B(tài)分解來處理同樣的實(shí)驗(yàn)數(shù)據(jù),由得到的本征模函數(shù)對比可以看出前者能有效地抑制經(jīng)驗(yàn)?zāi)B(tài)分解的端點(diǎn)效應(yīng)。然后再將故障齒輪振動(dòng)信號和正常齒輪振動(dòng)信號分別利用改進(jìn)經(jīng)驗(yàn)?zāi)B(tài)分解進(jìn)行處理,得到他們各自的本征模函數(shù),并對其進(jìn)行希爾伯變換,進(jìn)一步獲得齒輪在故障狀態(tài)和正常狀態(tài)下振動(dòng)信號的希爾伯特譜及邊際譜,從對比中可以明顯發(fā)現(xiàn)齒輪故障的存在,同時(shí)也進(jìn)一步證明了灰色GM(1,1)模型能夠有效地抑制經(jīng)驗(yàn)?zāi)B(tài)分解的端點(diǎn)效應(yīng)。
[Abstract]:With the rapid development of signal processing technology, signal time-frequency analysis method has become nonlinear. Non-stationary signal is one of the most important methods. It synthesizes the characteristics of signal from two different angles of time domain and frequency domain, and can understand the characteristic information of signal in time domain and frequency domain at the same time. It is an important breakthrough in the field of signal processing, in which empirical mode decomposition (EMD) is an empirical Mode Decomposition. EMD, a new time-frequency analysis method developed in recent years, was first proposed by N.E.Huang et al., a Chinese American in 1998. It has become one of the important ways of signal time-frequency analysis. In this paper, several modern time-frequency analysis methods and their advantages and disadvantages are briefly introduced, and the computational principles and shortcomings of empirical mode decomposition theory are studied. The main causes of endpoint effect in the process of dealing with nonlinear and non-stationary signals by empirical mode decomposition (EMD) are analyzed. A new method to suppress the end-point effect of empirical mode decomposition (EMD) is proposed, in which the discrete sequence prediction grey theory (GM-1) model is applied in the process of EMD. The end value of the decomposed discrete signal sequence is extended outwardly, and the new sequence after extension reflects the internal information and the development trend of the original signal well, which makes it possible to make use of cubic spline interpolation to form the new sequence. In the lower envelope, the endpoint effect is suppressed without contamination into the original discrete sequence, thus the real and effective eigenmode function can be decomposed. In addition, the gear is one of the important transmission components in mechanical transmission. The method of gear fault diagnosis plays an important role in the development of modern industry. When the gear has broken teeth, wear and pitting in operation, it will cause the gear strong meshing shock vibration. The vibration signal contains periodic fault shock vibration component. The improved empirical mode decomposition method is used to extract gear fault characteristics from gear meshing vibration signal. It provides a more reliable method for early prediction and diagnosis of gear fault diagnosis. The experiment is an important way to obtain data and verify whether the theory is correct. All the data in this paper come from the physical simulation experiment of gear fault. In this experiment, the gear meshing vibration signals in normal state and fault state were collected, and the experimental data were processed by software MATLAB. Based on the experimental data, the improved and unimproved empirical mode decomposition are used to deal with the same experimental data. The comparison of eigenmode function shows that the former can effectively suppress the endpoint effect of empirical mode decomposition. Then, the vibration signal of fault gear and the vibration signal of normal gear can be decomposed by improved empirical mode decomposition, respectively. Handle it. Their Eigenmode functions are obtained, and Hilbert transform is used to obtain the Hilbert spectrum and marginal spectrum of gear vibration signal in fault state and normal state. From the comparison, it can be found that the gear fault obviously exists, and it is further proved that the grey GM1 / 1) model can effectively suppress the end-point effect of empirical mode decomposition (EMD).
【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】：2011
【分類號】：TH165.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 田昊;唐力偉;田廣;;基于盲源分離的齒輪箱復(fù)合故障診斷研究[J];兵工學(xué)報(bào);2010年05期

2 許寶杰;張建民;徐小力;李建偉;;抑制EMD端點(diǎn)效應(yīng)方法的研究[J];北京理工大學(xué)學(xué)報(bào);2006年03期

3 魏秀業(yè);潘宏俠;;齒輪箱故障診斷技術(shù)現(xiàn)狀及展望[J];測試技術(shù)學(xué)報(bào);2006年04期

4 孫華剛;馮廣斌;曹登慶;毛向東;;經(jīng)驗(yàn)?zāi)B(tài)分解端點(diǎn)波形延拓改進(jìn)方法研究[J];電子測量與儀器學(xué)報(bào);2010年04期

5 安婧;伉大儷;郭海濤;楊志民;;時(shí)—頻分析方法在齒輪故障診斷中的應(yīng)用[J];信息技術(shù);2010年03期

6 王陽;劉紅彥;;頻譜分析在齒輪故障診斷中的應(yīng)用[J];石油和化工設(shè)備;2010年03期

7 徐龍?jiān)?芮執(zhí)元;馮瑞成;;基于Hilbert-Huang變換的齒輪故障診斷研究[J];機(jī)床與液壓;2010年03期

8 胡召音;灰色理論及其應(yīng)用研究[J];武漢理工大學(xué)學(xué)報(bào)(交通科學(xué)與工程版);2003年03期

9 申永軍,楊紹普,劉獻(xiàn)棟;齒輪故障診斷中的信號處理技術(shù)研究與展望[J];機(jī)械傳動(dòng);2004年03期

10 李輝;鄭海起;唐力偉;;Hilbert-Huang變換在齒輪裂紋故障診斷中的應(yīng)用[J];機(jī)械強(qiáng)度;2006年S1期

相關(guān)碩士學(xué)位論文 前3條

1 相小誼;基于Hilbert-Huang變換的信號分析及應(yīng)用[D];西安電子科技大學(xué);2008年

2 徐進(jìn)華;基于灰色系統(tǒng)理論的數(shù)據(jù)挖掘及其模型研究[D];北京交通大學(xué);2009年

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

，

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