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  本文選題：工作模態(tài)參數(shù)識別 + 小波變換��； 參考：《太原理工大學(xué)》2011年碩士論文

【摘要】：目前,基于振動的模態(tài)分析越來越受到科研機構(gòu)和企業(yè)單位的重視,而模態(tài)參數(shù)識別又是其中最重要、最基礎(chǔ)的一部分。由于實測的振動信號更能夠真實的反應(yīng)結(jié)構(gòu)本身的固有特性和邊界條件,因此工作模態(tài)參數(shù)識別就成為了模態(tài)參數(shù)識別新的研究和發(fā)展方向。 基于以上原因,本文主要對工作模態(tài)參數(shù)識別的小波分析方法進行了探索性研究,在該方法的研究過程中,主要做了以下工作： (1)論文系統(tǒng)地總結(jié)了近年來基于工作模態(tài)參數(shù)辨識方法的大量文獻(xiàn),對其研究意義、國內(nèi)外現(xiàn)狀和一些模態(tài)識別方法進行了研究,并給了較為全面的論述,確定了論文研究的內(nèi)容。 (2)論文從工作模態(tài)參數(shù)識別的角度出發(fā),對小波分析的基本理論進行整理、歸納和闡述。對小波時頻局部化的特點進行了理論推導(dǎo),分析了小波時、頻分辨率的關(guān)系。在滿足參數(shù)辨識條件的基礎(chǔ)上根據(jù)小波選取原則分析比.較了幾種小波的各種特性參數(shù),確定了選擇Morlet小波作為系統(tǒng)模態(tài)參數(shù)識別的母小波。 (3)論文對小波變換的脊線提取方法進行了深入的研究,為建立模態(tài)參數(shù)識別的小波辨識方法奠定了基礎(chǔ)。本文在平穩(wěn)相位理論的基礎(chǔ)上,建立了小波脊線提取的基本方法。通過對多種脊線提取方法的比較,選擇了蟻群算法作為小波變換脊線提取的方法。仿真驗證表明,蟻群算法不僅能夠很好的提取小波變換的脊線,同時具有很高的抗噪性。 (4)論文建立了基于小波變換的工作模態(tài)參數(shù)識別方法。通過一個三自由度系統(tǒng)的仿真算例,驗證了小波變換法識別模態(tài)的參數(shù)的可行性,通過與理論計算值比較驗證了小波變換法識別模態(tài)參數(shù)的精度。 (5)論文通過隨機白噪聲激勵下的懸臂梁來仿照它工作狀態(tài)下的振動,分別通過小波變換、Polymax、ITD、ARMA模型時間序列等方法對懸臂梁進行了模態(tài)參數(shù)識別。同時對小波變換、Polymax、ITD、ARMA模型時間序列分析法識別的結(jié)果做了比較,證明了工作模態(tài)參數(shù)識別的小波變換方法要優(yōu)于其他的幾種辨識方法。
[Abstract]:At present, modal analysis based on vibration is paid more and more attention by scientific research institutions and enterprises, and modal parameter identification is the most important and basic part of it. Because the measured vibration signals can be more true to the inherent characteristics and boundary conditions of the structure itself, the working modal parameter identification has become a new research and development direction of modal parameter identification. Based on the above reasons, this paper mainly studies the wavelet analysis method of working modal parameter identification. The main works are as follows: (1) this paper systematically summarizes a large number of literatures based on working modal parameter identification in recent years, and studies its research significance, domestic and foreign current situation and some modal identification methods. And give a more comprehensive discussion, determine the content of the paper. (2) from the point of view of the identification of working modal parameters, the basic theory of wavelet analysis is sorted out, summarized and elaborated. The characteristics of wavelet time-frequency localization are theoretically deduced and the relationship between wavelet time-frequency resolution and wavelet time-frequency resolution is analyzed. On the basis of satisfying the condition of parameter identification, the ratio is analyzed according to the principle of wavelet selection. Compared with the various characteristic parameters of several kinds of wavelets, we select Morlet wavelet as the mother wavelet to identify the modal parameters of the system. (3) in this paper, the ridge extraction method of wavelet transform is studied deeply. It lays a foundation for establishing wavelet identification method for modal parameter identification. On the basis of stationary phase theory, the basic method of wavelet ridge extraction is established in this paper. Ant colony algorithm is chosen as the method of ridge extraction based on wavelet transform. Simulation results show that the ant colony algorithm not only can extract the ridge of wavelet transform, but also has a high noise resistance. (4) the method of working modal parameter identification based on wavelet transform is established in this paper. The feasibility of identifying modal parameters by wavelet transform is verified by a simulation example of a three-degree-of-freedom system. The accuracy of the wavelet transform method in identifying modal parameters is verified by comparing with the theoretical results. (5) the vibration of the cantilever beam excited by random white noise is simulated in this paper. The modal parameters of the cantilever beam are identified by using the time series of the Polymax ITD ARMA model of wavelet transform. At the same time, the results of the time series analysis of the wavelet transform Polymaxax ITDU ARMA model are compared, and it is proved that the wavelet transform method of working modal parameter identification is superior to other identification methods.
【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

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