本文選題：旋轉機械 + 振動分析�。� 參考：《華北電力大學（北京）》2011年碩士論文

【摘要】：隨著現(xiàn)代化工業(yè)及科學技術的迅猛發(fā)展,旋轉機械在工業(yè)領域也呈現(xiàn)出巨大的變化,并起著越來越重要的作用。尤其是電力工業(yè)中的主要機械設備和輔機正向著大型化、自動化、高效率、機電一體化等方向發(fā)展,影響安全的因素也逐漸增多。因此,要保證這些大型旋轉機械安全,經濟運行,旋轉機械故障特征提取技術成為研究重點。本文主要研究了自回歸模型(Autoregression Model,簡稱AR模型),小波分析,短時傅里葉變換(Short-Time Fourier Transform,簡稱STFT)、維格納-威爾分布(Wigner-Ville Distribution,簡稱WVD)和希爾伯特黃變換(Hilbert-Huang Transform,簡稱HHT),并開發(fā)了旋轉機械振動信號分析系統(tǒng)。 針于AR模型,主要研究了如何確定模型的階數(shù),以及自相關估計、Burg法和改進的協(xié)方差法的分辨率對比,并采用軸承局部故障信號和齒輪故障信號,討論AR模型參數(shù)估計功率譜,結果發(fā)現(xiàn)能得到分辨率和方差性能較好的光滑譜線,能有效的提取故障特征。 本文研究了小波變換的基礎理論；研究了它在旋轉機械的奇異性信號,多種混合信號和含噪信號中的應用；并采用軸承局部故障信號和齒輪故障信號,討論小波分析在特征提取中的應用,最后發(fā)現(xiàn)小波分析可以很好地應用在旋轉機械故障信號特征提取中。 為了能提取信號頻率隨時間的變化信息,研究了時頻分析技術中的STFT、WVD和HHT的理論,討論了STFT和WVD與傅里葉變換的區(qū)別,并研究了STFT和WVD各自的特點：STFT的分辨效果受窗函數(shù)的影響,WVD分析多分量信號時受交叉項的的干擾。研究了HHT中Hilbert變換引起的端點效應,并采用周期延拓和對稱延拓兩種方法抑制端點效應。本文對三種時頻分析技術進行了對比,并將其應用在旋轉機械振動信號的特征提取中,驗證了時頻分析技術可以得到信號的頻率隨時間變化的信息。 最后,采用C++Builder和Matlab相結合的方法,開發(fā)了一個旋轉機械振動信號分析系統(tǒng),可以對信號進行自相關估計,Burg法估計,改進的協(xié)方差法估計,小波分析,STFT, WVD和HHT。其中,STFT, WVD和HHT是通過采用C++Builder調用Matlab引擎庫中的短時傅里葉變換函數(shù),維格納-威爾分布函數(shù),希爾伯特黃變換函數(shù)來實現(xiàn)的。
[Abstract]:With the rapid development of modern industry and science and technology, rotating machinery has shown great changes in the field of industry and plays an increasingly important role. Especially in the power industry, the main mechanical equipment and auxiliary machines are developing towards the direction of large-scale, automation, high efficiency, electromechanical integration and so on, and the factors affecting safety are also increasing gradually. Therefore, to ensure the safety and economic operation of these large rotating machinery, the fault feature extraction technology of rotating machinery has become the focus of research. In this paper, we mainly study the Autoregression Model (AR Model), wavelet analysis, Short-time Fourier transform (STFT), Wigner-Ville Distribution (WVD) and Hilbert-Huang transform (HHT) have been developed. Based on the AR model, this paper mainly studies how to determine the order of the model and the resolution comparison between the autocorrelation estimation Burg method and the improved covariance method, and discusses the power spectrum estimation of the AR model parameters by using the bearing local fault signal and the gear fault signal. The results show that smooth spectral lines with better resolution and variance can be obtained and fault features can be extracted effectively. In this paper, the basic theory of wavelet transform is studied, the application of wavelet transform in singularity signal, mixed signal and noisy signal of rotating machine is studied, and the bearing local fault signal and gear fault signal are adopted. The application of wavelet analysis in feature extraction is discussed. Finally, it is found that wavelet analysis can be applied to feature extraction of fault signals of rotating machinery. In order to extract the information of signal frequency varying with time, the theory of STFTWVD and HHT in time-frequency analysis is studied, and the difference between STFT and WVD and Fourier transform is discussed. The characteristics of STFT and WVD are studied respectively. The resolution effect of WVD is influenced by the window function and the crossover in the analysis of multi-component signals by WVD is studied. The endpoint effect caused by Hilbert transform in HHT is studied, and two methods, periodic continuation and symmetric continuation, are used to suppress the endpoint effect. In this paper, three kinds of time-frequency analysis techniques are compared and applied to feature extraction of vibration signals of rotating machinery. It is verified that time-frequency analysis technology can obtain information of signal frequency varying with time. Finally, a vibration signal analysis system for rotating machinery is developed by combining C Builder and Matlab. The system can be used to estimate signals by autocorrelation estimation, improved covariance method, wavelet analysis, STFT, WVD and HHT. Among them, STFT, WVD and HHT are realized by using C Builder to call short time Fourier transform function, Wigner distribution function and Hilbert yellow transform function in Matlab engine library.
【學位授予單位】：華北電力大學（北京）
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

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本文編號：2117753