本文選題：旋轉(zhuǎn)機(jī)械 + 振動(dòng)分析�。� 參考：《華北電力大學(xué)（北京）》2011年碩士論文

【摘要】：隨著現(xiàn)代化工業(yè)及科學(xué)技術(shù)的迅猛發(fā)展,旋轉(zhuǎn)機(jī)械在工業(yè)領(lǐng)域也呈現(xiàn)出巨大的變化,并起著越來(lái)越重要的作用。尤其是電力工業(yè)中的主要機(jī)械設(shè)備和輔機(jī)正向著大型化、自動(dòng)化、高效率、機(jī)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(zhuǎn)機(jī)械安全,經(jīng)濟(jì)運(yùn)行,旋轉(zhuǎn)機(jī)械故障特征提取技術(shù)成為研究重點(diǎn)。本文主要研究了自回歸模型(Autoregression Model,簡(jiǎn)稱AR模型),小波分析,短時(shí)傅里葉變換(Short-Time Fourier Transform,簡(jiǎn)稱STFT)、維格納-威爾分布(Wigner-Ville Distribution,簡(jiǎn)稱WVD)和希爾伯特黃變換(Hilbert-Huang Transform,簡(jiǎn)稱HHT),并開(kāi)發(fā)了旋轉(zhuǎn)機(jī)械振動(dòng)信號(hào)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(guān)估計(jì)、Burg法和改進(jìn)的協(xié)方差法的分辨率對(duì)比,并采用軸承局部故障信號(hào)和齒輪故障信號(hào),討論AR模型參數(shù)估計(jì)功率譜,結(jié)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(chǔ)理論；研究了它在旋轉(zhuǎn)機(jī)械的奇異性信號(hào),多種混合信號(hào)和含噪信號(hào)中的應(yīng)用；并采用軸承局部故障信號(hào)和齒輪故障信號(hào),討論小波分析在特征提取中的應(yīng)用,最后發(fā)現(xiàn)小波分析可以很好地應(yīng)用在旋轉(zhuǎn)機(jī)械故障信號(hào)特征提取中。 為了能提取信號(hào)頻率隨時(shí)間的變化信息,研究了時(shí)頻分析技術(shù)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(diǎn)：STFT的分辨效果受窗函數(shù)的影響,WVD分析多分量信號(hào)時(shí)受交叉項(xiàng)的的干擾。研究了HHT中Hilbert變換引起的端點(diǎn)效應(yīng),并采用周期延拓和對(duì)稱延拓兩種方法抑制端點(diǎn)效應(yīng)。本文對(duì)三種時(shí)頻分析技術(shù)進(jìn)行了對(duì)比,并將其應(yīng)用在旋轉(zhuǎn)機(jī)械振動(dòng)信號(hào)的特征提取中,驗(yàn)證了時(shí)頻分析技術(shù)可以得到信號(hào)的頻率隨時(shí)間變化的信息。 最后,采用C++Builder和Matlab相結(jié)合的方法,開(kāi)發(fā)了一個(gè)旋轉(zhuǎn)機(jī)械振動(dòng)信號(hào)分析系統(tǒng),可以對(duì)信號(hào)進(jìn)行自相關(guān)估計(jì),Burg法估計(jì),改進(jìn)的協(xié)方差法估計(jì),小波分析,STFT, WVD和HHT。其中,STFT, WVD和HHT是通過(guò)采用C++Builder調(diào)用Matlab引擎庫(kù)中的短時(shí)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(lái)實(shí)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(xué)位授予單位】：華北電力大學(xué)（北京）
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3

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本文編號(hào)：2117753