本文選題：局部特征尺度分解　切入點(diǎn)：自適應(yīng)　出處：《湖南大學(xué)》2014年博士論文　論文類(lèi)型：學(xué)位論文

【摘要】：機(jī)械設(shè)備狀態(tài)監(jiān)測(cè)和故障診斷對(duì)于保證機(jī)械設(shè)備的健康運(yùn)行、早期故障的預(yù)警以及故障發(fā)生的正確定位與診斷都有重要的理論和實(shí)際意義。機(jī)械設(shè)備振動(dòng)信號(hào)大都是非線性和非平穩(wěn)信號(hào)，因此，機(jī)械設(shè)備故障診斷的關(guān)鍵是如何從非線性和非平穩(wěn)信號(hào)中提取故障特征并進(jìn)行模式識(shí)別。時(shí)頻分析方法由于能夠同時(shí)提供振動(dòng)信號(hào)時(shí)域和頻域的局部信息而在機(jī)械故障診斷中得到了廣泛應(yīng)用。 近年來(lái)，小波變換、經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition, EMD)、局部均值分解(Local Mean Decomposition, LMD)等時(shí)頻分析方法由于特別適合處理機(jī)械振動(dòng)信號(hào)而被國(guó)內(nèi)外相關(guān)學(xué)者應(yīng)用到機(jī)械故障診斷領(lǐng)域，并取得了許多非�？上驳难芯砍晒�，但這些時(shí)頻分析方法都有各自不同的局限性。局部特征尺度分解(Local Characteristic-scale Decomposition, LCD)是一種新的非平穩(wěn)信號(hào)自適應(yīng)分析方法，該方法在定義瞬時(shí)頻率具有物理意義的單分量信號(hào)——內(nèi)稟尺度分量(Intrinsic Scale Component, ISC)基礎(chǔ)上，自適應(yīng)地將一個(gè)復(fù)雜信號(hào)分解為若干個(gè)ISC分量之和，從而得到原始信號(hào)完整的時(shí)頻分布。與EMD、LMD等方法相比，LCD在端點(diǎn)效應(yīng)的抑制、計(jì)算速度和分解效果等方面具有一定的優(yōu)越性。論文在國(guó)家自然科學(xué)基金項(xiàng)目（編號(hào)：51075131）的資助下，，對(duì)局部特征尺度分解方法進(jìn)行了深入的研究，對(duì)其理論進(jìn)行了完善，在此基礎(chǔ)上，將局部特征尺度分解方法及其理論應(yīng)用于旋轉(zhuǎn)機(jī)械故障診斷。 論文主要研究工作和創(chuàng)新性成果有： 1.對(duì)LCD方法的理論進(jìn)行了研究，解決了均值曲線定義存在的不足、模態(tài)混疊的抑制等問(wèn)題。 (1)將LCD方法與EMD進(jìn)行了對(duì)比分析，仿真和機(jī)械故障振動(dòng)信號(hào)的分析結(jié)果表明了LCD方法的優(yōu)越性； (2)針對(duì)LCD中均值曲線中直線連接極值會(huì)與數(shù)據(jù)交叉的問(wèn)題，提出了基于分段多項(xiàng)式的改進(jìn)LCD方法，并將其應(yīng)用于仿真和轉(zhuǎn)子碰摩故障振動(dòng)信號(hào)分析，結(jié)果表明了ILCD方法的有效性； (3)針對(duì)基于篩分的自適應(yīng)信號(hào)分解方法中由于均值曲線不同而導(dǎo)致分解結(jié)果差異的問(wèn)題，提出了一種新的非平穩(wěn)信號(hào)的自適應(yīng)分解方法——廣義局部特征尺度分解（Generalized LCD, GLCD），GLCD通過(guò)從不同均值曲線篩分的結(jié)果中選擇最優(yōu)分量，再對(duì)剩余信號(hào)重復(fù)篩分過(guò)程，從而保證了最終的分解結(jié)果也是最優(yōu)的。分別采用仿真和機(jī)械故障振動(dòng)信號(hào)將其與EMD、LCD方法進(jìn)行了對(duì)比，結(jié)果表明GLCD方法在正交性、分解能力等方面有一定的優(yōu)越性，從而能夠得到更好的分解結(jié)果。 (4)針對(duì)LCD分解過(guò)程中可能出現(xiàn)的模態(tài)混疊問(wèn)題，分別提出了部分集成和完備總體平均局部特征尺度分解等方法，對(duì)仿真和機(jī)械故障振動(dòng)信號(hào)的分析結(jié)果表明，所提出的方法能夠有效地抑制LCD的模態(tài)混疊現(xiàn)象。 2.對(duì)ISC分量的瞬時(shí)頻率估計(jì)方法進(jìn)行了研究，提出了兩種新的瞬時(shí)頻率估計(jì)方法和多分量信號(hào)解調(diào)方法。 (1)針對(duì)希爾伯特變換、能量算子解調(diào)和標(biāo)準(zhǔn)希爾伯特變換等常用的瞬時(shí)估計(jì)方法存在的不足，提出了一種新的瞬時(shí)頻率估計(jì)方法——經(jīng)驗(yàn)包絡(luò)法，仿真信號(hào)分析結(jié)果表明了其優(yōu)越性。同時(shí)，針對(duì)機(jī)械故障振動(dòng)信號(hào)的調(diào)制特性，提出了基于LCD的經(jīng)驗(yàn)包絡(luò)解調(diào)方法，并將其應(yīng)用于滾動(dòng)軸承的故障診斷，結(jié)果表明了所提出方法的有效性； (2)針對(duì)標(biāo)準(zhǔn)希爾伯特變換和直接正交法存在的問(wèn)題，提出了歸一化正交法。并針對(duì)多分量信號(hào)的解調(diào)問(wèn)題，提出了基于GLCD和歸一化正交的時(shí)頻分析方法，仿真和實(shí)驗(yàn)信號(hào)的分析結(jié)果表明了所提出方法的優(yōu)越性； 3.對(duì)LCD方法在機(jī)械故障診斷中的應(yīng)用進(jìn)行了研究，與其它數(shù)學(xué)方法相結(jié)合，提出了多種基于LCD的機(jī)械故障診斷方法，實(shí)驗(yàn)數(shù)據(jù)分析結(jié)果表明了LCD方法可以有效地應(yīng)用于機(jī)械故障診斷。 (1)在對(duì)多尺度模糊熵進(jìn)行改進(jìn)的基礎(chǔ)上，提出了基于LCD和模糊熵的振動(dòng)信號(hào)自適應(yīng)多尺度復(fù)雜性分析方法；在多尺度排列熵的基礎(chǔ)上，提出了基于LCD和排列熵的振動(dòng)信號(hào)自適應(yīng)多尺度隨機(jī)性檢測(cè)方法，并將它們應(yīng)用于機(jī)械故障振動(dòng)信號(hào)特征的提��； (2)將基于變量預(yù)測(cè)模型的模式識(shí)別(Variable Predictive Model based ClassDiscriminate, VPMCD)方法的應(yīng)用擴(kuò)展到機(jī)械故障診斷領(lǐng)域，VPMCD方法基于特征量之間的內(nèi)在關(guān)系建立預(yù)測(cè)模型，通過(guò)對(duì)特征量進(jìn)行預(yù)測(cè)，從而實(shí)現(xiàn)模式的分類(lèi)。在VPMCD的基礎(chǔ)上，結(jié)合LCD，提出了相應(yīng)的旋轉(zhuǎn)機(jī)械智能故障診斷方法。
[Abstract]:Mechanical equipment condition monitoring and fault diagnosis to ensure the healthy operation of mechanical equipment, has important theoretical and practical significance to correctly locate the fault early warning and fault early diagnosis. And the vibration signals of mechanical equipment are nonlinear and non-stationary signal, so the key to fault diagnosis of mechanical equipment is from nonlinear and non-stationary signal in fault feature extraction and pattern recognition. The time-frequency analysis method for local information can also provide the vibration signal in time domain and frequency domain is widely used in mechanical fault diagnosis.
In recent years, wavelet transform, empirical mode decomposition (Empirical Mode, Decomposition, EMD), the local mean decomposition (Local Mean Decomposition, LMD) and other methods as particularly suitable for processing of mechanical vibration signals by domestic and foreign scholars applied to mechanical fault diagnosis field frequency analysis, and made a lot of very gratifying results, but these the time-frequency analysis method has its own limitations. The local characteristic scale decomposition (Local Characteristic-scale Decomposition, LCD) is a new adaptive non-stationary signal analysis method, the single component of intrinsic scale components the signal has a physical meaning in the definition of instantaneous frequency (Intrinsic Scale Component, ISC) based on adaptive to be a complex signal is decomposed into several ISC components, and thus, the time-frequency distribution of original signal integrity is obtained. Compared with EMD, LMD, L Inhibition of CD in the end effect, has certain advantages of calculating speed and decomposition effect. Based on the project of National Natural Science Foundation (No. 51075131) under the support of local characteristic scale decomposition method is studied, the theory was improved based on the local characteristic scale decomposition the theory and method applied in fault diagnosis of rotating machinery.
The main research work and innovative achievements of the paper are as follows:
1. the theory of LCD method is studied, which solves the problem of the deficiency of the definition of the mean curve and the suppression of the modal aliasing.
(1) the LCD method and the EMD are compared and analyzed. The simulation and the analysis results of the mechanical fault vibration signal show the superiority of the LCD method.
(2) in view of the problem that the line connection extremum in the mean value curve of LCD will be intersecting data, an improved LCD method based on piecewise polynomial is proposed. It is applied to simulation and rotor rub impact fault vibration signal analysis, and the results show the effectiveness of ILCD method.
(3) based on adaptive signal decomposition method in screening due to the mean curve due to different decomposition results of different problems, put forward a new kind of non-stationary signal adaptive decomposition method -- generalized local characteristic scale decomposition (Generalized LCD, GLCD, GLCD) by selecting the optimal component from different mean curve screening results. And then the rest of the signal repeat screening process, so as to ensure the final result of the decomposition is optimal. By simulation and mechanical fault vibration signal with the EMD, compared with LCD method, the result showed that GLCD method in orthogonal, have certain superiority decomposition ability, which can get better decomposition results.
(4) for possible modal decomposition process of LCD mixing, respectively put forward some integrated and complete the overall average local characteristic scale decomposition method, the analysis results of the simulation and mechanical fault vibration signals show that the proposed modal method can effectively suppress LCD aliasing.
2. the method of instantaneous frequency estimation for ISC component is studied, and two new instantaneous frequency estimation methods and multi component signal demodulation methods are proposed.
(1) based on Hilbert transform, instantaneous energy operator demodulation and standard Hilbert transform popular estimation method and its shortcomings, puts forward a new method of instantaneous frequency estimation -- envelope method, signal analysis simulation results show its superiority. At the same time, modulation characteristics for mechanical fault vibration signal, puts forward experience envelope demodulation method based on LCD and its application in fault diagnosis of rolling bearing. The results show the effectiveness of the proposed method;
(2) according to the existing standard of Hilbert transform and direct orthogonal method, proposed the normalized orthogonal method. Aiming at the problem of multi - component signal demodulation, time-frequency analysis method GLCD and normalized orthogonal was put forward based on the analysis results of the simulation and experiment signals show that the proposed method is superior;
3., the application of LCD in mechanical fault diagnosis is studied. Combined with other mathematical methods, a variety of LCD based mechanical fault diagnosis methods are put forward. Experimental data analysis results show that LCD method can be applied to mechanical fault diagnosis effectively.
(1) improved based on multi-scale fuzzy entropy, adaptive method is proposed to analyze vibration signal LCD and fuzzy entropy based on multi scale complexity based on multiscale permutation entropy; on the proposed adaptive vibration signal LCD and multiscale permutation entropy method based on random detection and extraction, and their application on the characteristics of mechanical fault vibration signals;
(2) the pattern recognition based on variable prediction model (Variable Predictive Model based ClassDiscriminate, VPMCD) the application of the method is extended to the field of mechanical fault diagnosis, the VPMCD method based on the intrinsic relationship between the features to establish prediction model, through forecasting the characteristic quantities, so as to realize the pattern classification. On the basis of VPMCD, combined with LCD, put forward the corresponding intelligent fault diagnosis of rotating machinery.

【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】：2014
【分類(lèi)號(hào)】：TH165.3

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