本文選題：局部特征尺度分解　切入點：自適應　出處：《湖南大學》2014年博士論文　論文類型：學位論文

【摘要】：機械設備狀態(tài)監(jiān)測和故障診斷對于保證機械設備的健康運行、早期故障的預警以及故障發(fā)生的正確定位與診斷都有重要的理論和實際意義。機械設備振動信號大都是非線性和非平穩(wěn)信號，因此，機械設備故障診斷的關鍵是如何從非線性和非平穩(wěn)信號中提取故障特征并進行模式識別。時頻分析方法由于能夠同時提供振動信號時域和頻域的局部信息而在機械故障診斷中得到了廣泛應用。 近年來，小波變換、經驗模態(tài)分解(Empirical Mode Decomposition, EMD)、局部均值分解(Local Mean Decomposition, LMD)等時頻分析方法由于特別適合處理機械振動信號而被國內外相關學者應用到機械故障診斷領域，并取得了許多非�？上驳难芯砍晒@些時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)信號自適應分析方法，該方法在定義瞬時頻率具有物理意義的單分量信號——內稟尺度分量(Intrinsic Scale Component, ISC)基礎上，自適應地將一個復雜信號分解為若干個ISC分量之和，從而得到原始信號完整的時頻分布。與EMD、LMD等方法相比，LCD在端點效應的抑制、計算速度和分解效果等方面具有一定的優(yōu)越性。論文在國家自然科學基金項目（編號：51075131）的資助下，，對局部特征尺度分解方法進行了深入的研究，對其理論進行了完善，在此基礎上，將局部特征尺度分解方法及其理論應用于旋轉機械故障診斷。 論文主要研究工作和創(chuàng)新性成果有： 1.對LCD方法的理論進行了研究，解決了均值曲線定義存在的不足、模態(tài)混疊的抑制等問題。 (1)將LCD方法與EMD進行了對比分析，仿真和機械故障振動信號的分析結果表明了LCD方法的優(yōu)越性； (2)針對LCD中均值曲線中直線連接極值會與數據交叉的問題，提出了基于分段多項式的改進LCD方法，并將其應用于仿真和轉子碰摩故障振動信號分析，結果表明了ILCD方法的有效性； (3)針對基于篩分的自適應信號分解方法中由于均值曲線不同而導致分解結果差異的問題，提出了一種新的非平穩(wěn)信號的自適應分解方法——廣義局部特征尺度分解（Generalized LCD, GLCD），GLCD通過從不同均值曲線篩分的結果中選擇最優(yōu)分量，再對剩余信號重復篩分過程，從而保證了最終的分解結果也是最優(yōu)的。分別采用仿真和機械故障振動信號將其與EMD、LCD方法進行了對比，結果表明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果。 (4)針對LCD分解過程中可能出現(xiàn)的模態(tài)混疊問題，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，對仿真和機械故障振動信號的分析結果表明，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ISC分量的瞬時頻率估計方法進行了研究，提出了兩種新的瞬時頻率估計方法和多分量信號解調方法。 (1)針對希爾伯特變換、能量算子解調和標準希爾伯特變換等常用的瞬時估計方法存在的不足，提出了一種新的瞬時頻率估計方法——經驗包絡法，仿真信號分析結果表明了其優(yōu)越性。同時，針對機械故障振動信號的調制特性，提出了基于LCD的經驗包絡解調方法，并將其應用于滾動軸承的故障診斷，結果表明了所提出方法的有效性； (2)針對標準希爾伯特變換和直接正交法存在的問題，提出了歸一化正交法。并針對多分量信號的解調問題，提出了基于GLCD和歸一化正交的時頻分析方法，仿真和實驗信號的分析結果表明了所提出方法的優(yōu)越性； 3.對LCD方法在機械故障診斷中的應用進行了研究，與其它數學方法相結合，提出了多種基于LCD的機械故障診斷方法，實驗數據分析結果表明了LCD方法可以有效地應用于機械故障診斷。 (1)在對多尺度模糊熵進行改進的基礎上，提出了基于LCD和模糊熵的振動信號自適應多尺度復雜性分析方法；在多尺度排列熵的基礎上，提出了基于LCD和排列熵的振動信號自適應多尺度隨機性檢測方法，并將它們應用于機械故障振動信號特征的提取； (2)將基于變量預測模型的模式識別(Variable Predictive Model based ClassDiscriminate, VPMCD)方法的應用擴展到機械故障診斷領域，VPMCD方法基于特征量之間的內在關系建立預測模型，通過對特征量進行預測，從而實現(xiàn)模式的分類。在VPMCD的基礎上，結合LCD，提出了相應的旋轉機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.

【學位授予單位】：湖南大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TH165.3

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