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  本文選題：非平穩(wěn)信號 + 時頻聯(lián)合分析��； 參考：《哈爾濱工業(yè)大學(xué)》2016年博士論文

【摘要】：機械結(jié)構(gòu)的振動信號中包含著結(jié)構(gòu)本身的參數(shù)信息,也包含著復(fù)雜的故障信息。通過對振動信號進行深入分析處理可以有效地獲得結(jié)構(gòu)的模態(tài)參數(shù)、激勵方式和故障類型等信息。傳統(tǒng)的傅里葉分析方法已經(jīng)成功地將振動信號從時域轉(zhuǎn)換為頻域,但只能處理平穩(wěn)的信號。對于頻率特征或統(tǒng)計特性隨時間變化的隨機振動信號,傅里葉分析則變得不再適合。自然界和工程應(yīng)用中,非線性和非平穩(wěn)信號十分常見,經(jīng)典的線性和平穩(wěn)性假設(shè)在很多情況下并不能準確地描述這些特性,特別是時變系統(tǒng)和含有故障的系統(tǒng),其時變參數(shù)和故障特征往往包含在非平穩(wěn)的信號特性中,因此,對非平穩(wěn)信號頻率隨時間變化的特征進行分析變得尤為重要。近年來,許多學(xué)者針對非平穩(wěn)信號處理方法展開了研究,其中最有效的分析手段是時頻聯(lián)合分析,它可以同時表述信號的時域和頻域特征。時頻分析手段包括經(jīng)驗?zāi)Ｊ椒纸夥�、小波變換、Wigner-Ville分布等非參數(shù)化方法以及自適應(yīng)調(diào)頻小波分解等參數(shù)化方法,這些方法都能表示信號的頻率隨時間變化的特性。本文基于上述各種時頻分析方法,對時頻聯(lián)合分析理論進行系統(tǒng)地分析,并比較了各種方法的優(yōu)勢和局限性。在此基礎(chǔ)上,提出了一些改進方法,分別利用仿真和實際信號進行了有效性和精度驗證。主要研究內(nèi)容分為以下幾個方面:首先,論文對目前較熱門的幾種時頻分析方法進行了綜述,并通過實例比較了各自的適用范圍和優(yōu)缺點。其次,針對經(jīng)驗?zāi)Ｊ椒纸獯嬖诘哪�態(tài)混疊問題,引入解析模態(tài)分解,并針對解析模態(tài)分解抗噪性能較弱的問題,利用離散小波變換對信號進行降噪處理。仿真試驗表明,該方法能在一定噪聲條件下準確地辨識出線性系統(tǒng)的剛度、阻尼等模態(tài)參數(shù),并且能根據(jù)其時頻特性判斷系統(tǒng)是否存在非線性和確定非線性類型。再次,建立了一套完整的基于連續(xù)小波變換的時變系統(tǒng)模態(tài)參數(shù)辨識算法。該算法利用Morlet小波尺度圖提取小波脊和骨架曲線,再利用小波脊辨識系統(tǒng)的時變剛度,利用小波骨架包絡(luò)辨識時變阻尼,并通過單自由度和二自由度系統(tǒng)進行了仿真驗證。仿真系統(tǒng)包含了實際工程中普遍存在的參數(shù)突變和漸變情況,結(jié)果表明,本文的辨識方法能有效地識別系統(tǒng)時變參數(shù)。第四,研究了自適應(yīng)線性調(diào)頻小波變換在處理非線性調(diào)頻信號時的局限性,并針對性地提出了一種改進算法。該方法通過約束最優(yōu)線性調(diào)頻小波的時域長度來減小分段逼近帶來的辨識誤差。仿真結(jié)果表明,改進算法對提升瞬時頻率辨識精度效果明顯,特別是針對被分析信號頻率變化較快的情況。第五,針對Wigner-Ville分布的交叉項問題展開了研究。提出了一種既保留Wigner-Ville分布高時頻分辨率,同時又有效抑制交叉項的方法:利用小波尺度圖構(gòu)造時頻濾波器,對Wigner-Ville分布進行時頻濾波。仿真和實際信號分析結(jié)果表明,該方法對線性調(diào)頻和非線性調(diào)頻信號都具有很強的分析能力,且計算量較小,十分適合工程應(yīng)用。最后,利用三個工程實例,進一步論證了論文提出的幾種時頻分析方法及其改進方案的有效性。工程實例包括列車輪對多邊形磨損后的振動信號、列車-橋梁系統(tǒng)振動信號和蝙蝠回聲定位信號等。
[Abstract]:The vibration signal of the mechanical structure contains the parameter information of the structure itself, and also contains complex fault information. Through the deep analysis and processing of the vibration signal, the modal parameters, the mode of excitation and the type of the fault can be effectively obtained. The traditional Fourier analysis method has successfully transferred the vibration signal from the time domain. The Fourier analysis is no longer suitable for the random vibration signals of frequency characteristics or statistical characteristics with time. In nature and engineering applications, nonlinear and non-stationary signals are very common, and the classical linear peace stability hypothesis can not be accurately described in many cases. Some characteristics, especially the time-varying system and the system containing the fault, are often included in the nonstationary signal characteristics. Therefore, it is very important to analyze the characteristics of the nonstationary signal frequency with time. In recent years, many scholars have studied the nonstationary signal processing methods, among which the most important The effective analysis method is the time frequency joint analysis, which can simultaneously express the time and frequency characteristics of the signal. The time-frequency analysis means include the empirical mode decomposition method, the wavelet transform, the Wigner-Ville distribution and other parametric methods, such as the adaptive frequency modulation wavelet decomposition and so on. These methods can all express the frequency of the signal with time. Based on the various time-frequency analysis methods mentioned above, this paper systematically analyzes the theory of time frequency joint analysis, and compares the advantages and limitations of various methods. On this basis, some improvement methods are proposed, and the effectiveness and precision of the simulation and actual signals are used respectively. The main research contents are divided into the following aspects: First of all, this paper summarizes several popular time frequency analysis methods, and compares their respective applications and advantages and disadvantages through examples. Secondly, the analytical modal decomposition is introduced to the modal decomposition of empirical mode decomposition, and the discrete wavelet transform is used to solve the problem of weak anti noise performance of analytical modal decomposition. The simulation test shows that the method can identify the stiffness, damping and other modal parameters of the linear system accurately under certain noise conditions, and can determine whether the system has nonlinear and nonlinear types according to its time-frequency characteristics. Thirdly, a set of complete time variable system based on continuous wavelet transform is established. The algorithm uses the Morlet wavelet scale map to extract the wavelet ridge and the skeleton curve, and then uses the wavelet ridge identification system to identify the time-varying stiffness, uses the wavelet skeleton envelope to identify the time-varying damping, and through the single degree of freedom and two degree of freedom system, the simulation system is carried out. The simulation system contains the general existence of the actual engineering. The results show that the identification method in this paper can effectively identify the time-varying parameters of the system. Fourth, the limitations of adaptive LFM wavelet transform in the processing of Nonlinear FM signals are studied, and an improved algorithm is proposed. The method can restrain the time domain length of the optimal linear frequency modulation wavelet. To reduce the identification error caused by the piecewise approximation, the simulation results show that the improved algorithm is effective in improving the accuracy of the instantaneous frequency identification, especially in the case of the fast change in the frequency of the analyzed signal. Fifth, a study is carried out for the cross term problem of the Wigner-Ville distribution. A high time frequency division of the Wigner-Ville distribution is proposed. The method of discrimination and effective suppression of cross terms: using the wavelet scale to construct time frequency filter to filter the Wigner-Ville distribution. The simulation and actual signal analysis show that the method has strong analytical energy for both linear frequency modulation and Nonlinear FM signals, and the calculation is small. Finally, it is very suitable for engineering applications. By using three engineering examples, several time frequency analysis methods and the effectiveness of the improved scheme are further demonstrated. The engineering examples include the vibration signals of the train wheel pair after polygon wear, the vibration signal of the train bridge system and the echo location signal of the bat.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TH113.1;TN911.7

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