發(fā)布時(shí)間：2018-07-05 05:42

  本文選題：自適應(yīng)最稀疏時(shí)頻分析 + 廣義解調(diào)時(shí)頻分析��； 參考：《湖南大學(xué)》2016年博士論文

【摘要】：機(jī)械設(shè)備狀態(tài)監(jiān)測(cè)和故障診斷對(duì)于保證設(shè)備健康運(yùn)行具有重要的作用。隨著科學(xué)技術(shù)的發(fā)展,現(xiàn)代機(jī)械設(shè)備越來(lái)越復(fù)雜,對(duì)機(jī)械故障診斷新技術(shù)和新方法的研究具有重要的意義。機(jī)械故障診斷技術(shù)的核心是故障特征的提取,設(shè)備運(yùn)行中產(chǎn)生的振動(dòng)信號(hào)包含了大量的設(shè)備狀態(tài)信息,因此,基于振動(dòng)的診斷方法是非常有效的機(jī)械故障診斷方法。然而,機(jī)械振動(dòng)信號(hào)比較復(fù)雜,常表現(xiàn)為非平穩(wěn)、多分量、多調(diào)制特征,且經(jīng)常會(huì)受到噪聲干擾。因此,研究一種合適的能夠準(zhǔn)確提取故障特征信息的信號(hào)分析處理方法具有重要的實(shí)際意義。自適應(yīng)時(shí)頻分析方法在信號(hào)分解中根據(jù)信號(hào)本身的特性自動(dòng)選擇基函數(shù)或參數(shù),能夠有效提取機(jī)械故障振動(dòng)信號(hào)的本質(zhì)特征,因此在機(jī)械故障診斷中得到了廣泛的應(yīng)用。自適應(yīng)最稀疏時(shí)頻分析(Adaptive and Sparsest Time-Frequency Analysis,ASTFA)方法是一種新的自適應(yīng)時(shí)頻分析方法,它以分解得到的單分量個(gè)數(shù)最少為優(yōu)化目標(biāo),以單分量的瞬時(shí)頻率具有物理意義為約束條件,通過(guò)求解最優(yōu)化問(wèn)題將信號(hào)自適應(yīng)地分解為若干個(gè)內(nèi)稟模態(tài)函數(shù)之和。ASTFA方法結(jié)合了EMD(Empirical Mode Decomposition,EMD)方法中將信號(hào)自適應(yīng)地分解為若干個(gè)內(nèi)稟模態(tài)函數(shù)之和與稀疏分解中在過(guò)完備字典庫(kù)中尋優(yōu)以獲得信號(hào)稀疏分解的優(yōu)點(diǎn)。論文在國(guó)家自然科學(xué)基金項(xiàng)目(編號(hào):51375152)的資助下,對(duì)ASTFA方法的理論進(jìn)行了研究與完善,并將ASTFA方法應(yīng)用于機(jī)械設(shè)備故障診斷。論文的主要研究工作有:(1)對(duì)ASTFA方法進(jìn)行了研究,將ASTFA方法與EMD、LCD(Local Characteristic-scale Decomposition,LCD)、LMD(Local Mean Decomposition,LMD)方法進(jìn)行了對(duì)比,其分解結(jié)果的正交性、精確性等評(píng)價(jià)指標(biāo)優(yōu)于EMD、LCD、LMD方法;對(duì)ASTFA方法的分解能力進(jìn)行了研究;對(duì)ASTFA方法的抗模態(tài)混疊性能進(jìn)行了研究,仿真和轉(zhuǎn)子故障信號(hào)的分析驗(yàn)證了ASTFA方法在抑制模態(tài)混淆方面的優(yōu)勢(shì)。(2)從瞬時(shí)頻率計(jì)算對(duì)ASTFA方法進(jìn)行理論解釋。ASTFA方法與EMD、LCD、LMD等方法具有理論共性,都是將復(fù)雜信號(hào)自適應(yīng)地分解為具有調(diào)幅部分和調(diào)頻部分乘積形式的單分量信號(hào)之和。但是,ASTFA方法可以直接計(jì)算信號(hào)的瞬時(shí)頻率,不用通過(guò)歸一化的方法得到純調(diào)頻信號(hào),再進(jìn)行瞬時(shí)頻率計(jì)算,避免了極值點(diǎn)處存在的波動(dòng)和估計(jì)誤差,具有明顯的優(yōu)越性。(3)從濾波器設(shè)計(jì)對(duì)ASTFA方法進(jìn)行解釋。ASTFA方法將信號(hào)由時(shí)間域轉(zhuǎn)化到相位域,基于數(shù)據(jù)本身相位函數(shù)的驅(qū)動(dòng)來(lái)設(shè)計(jì)自適應(yīng)濾波器,即:通過(guò)選擇合適的初始相位函數(shù)和平滑度控制參數(shù)來(lái)設(shè)計(jì)符合要求的濾波器,從而實(shí)現(xiàn)信號(hào)的自適應(yīng)分解。(4)針對(duì)ASTFA方法初始相位函數(shù)的選擇問(wèn)題,提出了基于一維精確搜索的ASTFA方法和基于遺傳算法(Genetic Algorithm,GA)的ASTFA方法。兩種改進(jìn)的ASTFA方法都是基于初始相位函數(shù)的最優(yōu)選擇而提出,解決了原始ASTFA方法中初始相位函數(shù)選擇問(wèn)題。(5)研究了ASTFA方法在齒輪故障診斷中的應(yīng)用,提出了基于ASTFA的瞬時(shí)幅值譜和瞬時(shí)頻率譜方法;提出了基于ASTFA的多尺度模糊熵偏均值(Partial Mean MFE,PMMFE)的齒輪故障診斷方法;研究了ASTFA方法在齒輪箱復(fù)合故障診斷中的應(yīng)用;研究了ASTFA方法在行星齒輪箱故障診斷中的應(yīng)用。(6)研究了ASTFA方法在變速工況下機(jī)械設(shè)備故障診斷中的應(yīng)用。提出了基于ASTFA的階次方法,基于ASTFA的階次方法能夠準(zhǔn)確提取變速齒輪的故障特征信息;提出了基于ASTFA的廣義解調(diào)方法,ASTFA方法可以提供廣義解調(diào)方法所需的解調(diào)相位函數(shù),解決了廣義解調(diào)時(shí)頻分析方法中相位函數(shù)的選擇問(wèn)題,基于ASTFA的廣義解調(diào)方法能夠有效提取變速工況下的滾動(dòng)軸承故障特征信息。
[Abstract]:The state monitoring and fault diagnosis of mechanical equipment plays an important role in ensuring the healthy operation of the equipment. With the development of science and technology, modern machinery and equipment are becoming more and more complex. It is of great significance to study the new technology and new methods of mechanical fault diagnosis. The core of the mechanical fault diagnosis technology is the extraction of the fault features and the operation of the equipment. The vibration signals produced contain a lot of equipment state information. Therefore, the vibration based diagnosis method is a very effective method of mechanical fault diagnosis. However, the mechanical vibration signals are complex, often characterized by non stationary, multicomponent and multi modulation features, and often suffer from noise interference. Therefore, a suitable method can be used to accurately extract the vibration signals. The signal analysis and processing method of fault feature information is of great practical significance. The adaptive time frequency analysis method can automatically select the basic function or parameters according to the characteristics of the signal itself in the signal decomposition. It can effectively extract the essential characteristics of the mechanical fault vibration signal, so it has been widely used in the mechanical fault diagnosis. The Adaptive and Sparsest Time-Frequency Analysis (ASTFA) method is a new adaptive time-frequency analysis method. It takes the least number of single components of the decomposition as the optimization target, and the single component instantaneous frequency has physical meaning as the constraint condition. The signal is decomposed to the signal adaptively by solving the optimization problem. Several intrinsic modal functions and.ASTFA methods combine the EMD (Empirical Mode Decomposition, EMD) method to decompose the signal adaptively into the sum of several intrinsic modal functions and in the sparse decomposition in the overcomplete dictionary library to obtain the advantages of the signal sparse decomposition. 5152) the theory of ASTFA method is studied and perfected, and the ASTFA method is applied to the fault diagnosis of mechanical equipment. The main research work of this paper is as follows: (1) the method of ASTFA is studied, and the method of ASTFA is carried out with EMD, LCD (Local Characteristic-scale Decomposition, LCD), LMD (Local). In contrast, the evaluation indexes such as orthogonality and accuracy of the decomposition results are superior to the EMD, LCD and LMD methods; the decomposition ability of the ASTFA method is studied; the anti modal aliasing performance of the ASTFA method is studied. The simulation and the analysis of the rotor fault signal verify the advantage of the ASTFA method in the suppression of modal confusion. (2) from the instantaneous frequency meter. The theoretical explanation of the ASTFA method is based on the theoretical generality of the.ASTFA method and EMD, LCD, LMD and so on. All of these are the sum of a single component signal which adaptively decomposes the complex signal into the amplitude modulation part and the FM part product. However, the ASTFA method can directly calculate the instantaneous frequency of the signal without the normalization method. FM signals, then calculate the instantaneous frequency, avoid the fluctuation and estimation error at the extreme point, and have obvious superiority. (3) from the design of the filter design to the ASTFA method, the.ASTFA method transforms the signal from the time domain to the phase domain, and the adaptive filter is designed based on the phase function of the data itself, that is, through the selection of the filter. Select the appropriate initial phase function and the skid control parameter to design the filter that conforms to the requirement, thus realizing the adaptive decomposition of the signal. (4) in view of the selection of the initial phase function of the ASTFA method, the ASTFA method based on one dimensional precise search and the ASTFA method based on the genetic algorithm (Genetic Algorithm, GA) are proposed. The two improved methods are improved. The ASTFA method is based on the optimal selection of initial phase function, and solves the initial phase function selection problem in the original ASTFA method. (5) the application of ASTFA method in gear fault diagnosis is studied. The instantaneous amplitude spectrum and instantaneous frequency spectrum method based on ASTFA are proposed, and the multiscale fuzzy entropy partial mean (Pa) based on ASTFA (Pa) is proposed. The gear fault diagnosis method of rtial Mean MFE, PMMFE), the application of ASTFA method in the complex fault diagnosis of gear box, and the application of ASTFA in the fault diagnosis of the planetary gearbox. (6) the application of the ASTFA method in the fault diagnosis of the mechanical equipment under the variable speed condition is studied. The order method based on ASTFA, based on ASTF, is proposed. The order method of A can accurately extract the fault feature information of the transmission gear. A generalized demodulation method based on ASTFA is proposed. The ASTFA method can provide the demodulation phase function required by the generalized demodulation method, and solve the problem of the selection of the phase function in the generalized demodulation time frequency analysis method. The generalized demodulation method based on the ASTFA can be effectively extracted. Fault feature information of rolling bearing under variable speed conditions.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TH17

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