發(fā)布時間：2018-05-01 12:17

  本文選題：人工化學反應優(yōu)化算法 + 化學反應優(yōu)化��； 參考：《湖南大學》2014年博士論文

【摘要】：滾動軸承廣泛應用于機械行業(yè),故對滾動軸承故障診斷技術的研究具有重要意義。滾動軸承的故障診斷實際上是一個模式識別的過程,其關鍵是故障的特征提取和狀態(tài)識別。 時頻分析法是常用于振動信號的特征提取方法,其分為線性分析法和非線性分析法兩種類型。線性時頻分析方法包括短時傅立葉變換(STFT), Gabor變換和小波變換。非線性時頻分析方法包括離散傅立葉變換(DFT), Wigner-Ville分布,Choi-Wiliam分布。STFT通過對信號的局部化時頻分析建立時域和頻域兩者之間的關系,但信號的高頻和低頻分量又是由海森堡不確定性原理確定。Wigner-Ville分布是一種更加直觀和合理的非線性時頻分布,同時它還能夠描述信號的瞬時功率譜,但其在卷積過程產生的虛假分量,造成信號時頻特征的混亂。傅立葉變換將信號分解為一系列不同頻率段的正弦波,而小波變換將信號分解成它的小波基。而滾動軸承的振動信號是非線性和非平穩(wěn)的,因此以上方法在分析非平穩(wěn)和非線性信號時都具有一定的局限性。希爾伯特—黃變換(HHT)是一種自適應時頻分析方法,包括經驗模態(tài)分解(EMD)和Hilbert譜分析(HAS)兩個部分。EMD是依據信號自身的時間尺度特征將信號分解為有限個單分量信號,稱之為內稟模態(tài)函數(IMF)。該方法在非線性非平穩(wěn)數據的分析中具有較大的潛力,尤其是在分析典型的時頻能量交涉信號中,但是,EMD的端點效應,模態(tài)混淆,過包絡與欠包絡,負頻率,瞬時頻率,以及缺乏一定的理論基礎等一系列問題,仍需要進一步研究。 最近Smith提出了一種新的自適應時頻分析方法Local Mean Decomposition,局部均值分解(LMD)。LMD的原理是基于滑動平均處理將一個復雜多分量信號分解為若干乘積函數分量(PF),所使用的滑動步長(MA)對分解結果具有重要影響。此外,純調頻(FM)信號的標準也會對該方法的性能產生影響。與EMD方法相比,LMD具有更多的優(yōu)勢,如更少的迭代次數,更平穩(wěn)的端點效應和更高的瞬時頻率分辨率。由LMD分解得到的局部均值不受過包絡和欠包絡的影響,同時LMD局部幅值比EMD上、下包絡線更具有振蕩特性。因此,局部均值和局部幅度都基于信號的局部特征時間尺度。 本文研究了另一種自適應時頻分析方法Local Characteristic-Scale Decomposition即局部特征尺度分解(LCD)。通過LCD方法可以將信號分解為若干內稟尺度分量(ISC),每一個ISC分量都包含有信號的局部特征,因此通過該方法能夠提取到更精確和有效的原始信號特征信息。LCD在減少端點效應和迭代時間和瞬時特性的精度等方面的表現都要優(yōu)于EMD。此外LCD能夠改善EMD分解信號產生的邊際效應。根據上述優(yōu)點,本文提出了基于LMD和LCD的滾動軸承故障診斷方法。 滾動軸承故障診斷實際上是一個狀態(tài)識別過程。狀態(tài)識別采用統(tǒng)計學習理論,監(jiān)督與非監(jiān)督分類。狀態(tài)識別常用的狀態(tài)識別方法有人工神經網絡(Artificial neural network, ANN^和支持向量機(Support Vector Machine, SVM)。 ANN主要缺點是難以確定網絡結構和參數,還需要大量樣本,而在實際中很難獲得大量的樣本。另外,收斂速度慢也大大增加了計算時間。SVM是一個基于統(tǒng)計學習理論和結構風險最優(yōu)原則的有效的模式識別方法,不僅能解決ANN中存在的過擬合、局部最優(yōu)、收斂速度慢等問題,針對小樣本還具有很好的泛化概括能力。SVM被廣泛的應用于模式識別和其他領域。然而,SVM的參數選擇對分類效果有很大的影響,而參數的選擇實際上是一個優(yōu)化過程,因此優(yōu)化算法被應用于SVM的參數選擇。遺傳算法(Genetic algorithm,GA)和粒子群算法(Particle swarm optimization, PSO)都應用于SVM參數的優(yōu)化。GA算法具有收斂速度慢,容易丟失局部最優(yōu)解等問題。此外,GA并不能解決特定的優(yōu)化問題以及變種問題。PSO算法在解決問題時有容易描述、容易實現、收斂速度快等優(yōu)勢,但是存在不能有效避免過早收斂的缺陷。 近來,一種新的基于化學反應原理的優(yōu)化算法被提出后,經驗證在很多方面都優(yōu)于其它優(yōu)化算法�；瘜W反應優(yōu)化算法的思路來自化學反應的發(fā)生,模仿化學反應中分子的微觀運動,通過利用化學反應生成物具有最低能量的現象而實現優(yōu)化�；诨瘜W反應原理的優(yōu)化方法有兩種算法：一種是化學反應優(yōu)化(Chemical Reaction Optimization, CRO), CRO原理是基于系統(tǒng)的勢能,當勢能降低到最低限度時,反應系統(tǒng)會逐漸達到平衡狀態(tài),因此將勢能作為最小化問題的目標函數可行的；另一種是人工反應優(yōu)化算法(Artificial Chemical Reaction Optimization Algorithm, ACROA),焓和熵可以作為最小化和最大化問題的目標函數(狀態(tài)函數)。焓取決于物質的化學性質,溫度與壓力的狀態(tài),而熵用來測量化學系統(tǒng)組件的隨機性或病癥。ACROA具有一個參數,初始反應物,因此這種方法很容易使用。論文分別將CRO和ACROA應用于SVM的參數優(yōu)化。結果表明：基于化學反應優(yōu)化的支持向量機(Chemical Reaction Optimization-Support Vector Machine, CRO-SVM)和基于人工反應優(yōu)化算法的支持向量機(Artificial Chemical Reaction Optimization Algorithm-Support Vector Machine, ACROA-SVM)在解決分類問題上都優(yōu)于基于遺傳算法的支持向量機(Genetic algorithm-Support vectormachine, GA-SVM)和基于粒子群算法的支持向量機(Particle swarm optimization-Support vector machine, GA-SVM)。在此基礎上,論文分別將局部均值分解(Local mean decomposition, LMD)和局部特征尺度分解(Local characteristic-scale decomposition, LCD)與ACROA-SVM、CRO-SVM相結合應用于滾動軸承故障診斷。 論文主要工作和創(chuàng)新點如下： 1.對LMD和LCD兩種時頻分析方法進行了研究,分別將LMD、LCD與經驗模態(tài)分解(Empirical mode decomposition, EMD)方法進行了對比分析,仿真信號和滾動軸承故障實驗信號的分析結果表明,相對于EMD方法,LMD和LCD在計算效率的端點效應等方面具有優(yōu)越性。 2.對化學反應算法進行了理論研究,分析了GA和PSO這兩種啟發(fā)式算法的局限性,闡述了CRO和ACROA算法的原理及其化學反應過程,并提出了CRO和ACROA算法的參數。將CRO和ACROA和上述啟發(fā)式算法進行了對比分析,總結了CRO和ACROA算法的優(yōu)缺點。 3.提出了基于CRO, ACROA的SVM參數優(yōu)化方法。在支持向量機中,泛化能力以及最小訓練誤差和最小模型復雜性之間的權衡是由內核參數和正則常數C決定,核函數參數定義從輸入空間到輸出空間之間的非線性映射.如果這些參數沒有正確選擇,SVM的性能就會減弱。本文采用CRO, ACROA對核參數和正則常數C進行優(yōu)化,結果表明,CRO, ACROA相較于GA和PSO來說在訓練速度和分類率方面有更好的性能,能獲得最佳優(yōu)化效果。 4.將CRO, ACROA-SVM和LMD、LCD等方法相結合對滾動軸承進行故障診斷。 (1)提出了一種基于LCD能量熵,ACROA算法設計的支持向量機簡稱LCD-ACROA-SVM)的滾動軸承故障診斷。首先,將振動加速度信號分解成若干個內稟尺度分量,然后,提出LCD能量熵的概念。其次,從包含主要故障信息的內稟尺度分量中提取能量特征作為支持向量機分類器的輸入向量。最后,提出ACROA-SVM分類器用于識別滾動軸承故障模式。對內圈故障和外圈故障的滾動軸承進行分析,結果表明：基于ACROA-SVM的診斷方法和采用LCD方法提取不同頻帶能量水平能夠準確有效地識別滾動軸承故障模式,提出的方法要明顯優(yōu)于經驗模態(tài)分解方法,而且更加節(jié)省時間。 (2)提出了一種基于LMD和ACROA-SVM的滾動軸承故障診斷(簡稱LMD-ACROA-SVM)首先,采用局部均值分解方法將從滾動軸承中提取的原調制振動信號分解成若干個PF分量,其次,在包含主要故障信息的PF分量的包絡譜中,不同故障特征頻率處振幅的比值被定義為特征振幅比。最后,將特征振幅比作為ACROA-SVM分類器的輸入并對滾動軸承的故障模式進行識別。結果表明：與LMD方法相結合的ACROA-SVM分類器可以有效地提高故障診斷的準確率,并且耗時少。 (3)提出了一種新的基于LCD和CRO-SVM的滾動軸承故障診斷,簡稱LCD-CRO-SVM。首先,采用LCD方法將滾動軸承原始振動信號分解成若干個內稟尺度分量之和。其次,在一系列內稟尺度分量的包絡譜中,計算不同故障特征頻率處的振幅比。最后,將這些振幅比作為CRO-SVM分類器的輸入。實驗結果表明：相比于其他方法,與LCD方法相結合的CRO-SVM分類器能夠獲得更高的分類精度和需要更少的時間。
[Abstract]:Rolling bearings are widely used in the machinery industry, so it is of great significance to the research of fault diagnosis technology of rolling bearings. The fault diagnosis of rolling bearings is actually a process of pattern recognition, and the key is the feature extraction and state recognition of the fault.
Time frequency analysis is a feature extraction method commonly used for vibration signals. It is divided into two types: linear analysis and nonlinear analysis. Linear time-frequency analysis methods include short time Fu Liye transform (STFT), Gabor transform and wavelet transform. The nonlinear time-frequency analysis method includes discrete Fourier transform (DFT), Wigner-Ville distribution, and Choi-Wiliam points. .STFT establishes the relationship between the time domain and the frequency domain by the localization time frequency analysis of the signal, but the high frequency and low frequency components of the signal are also determined by the Heisenberg uncertainty principle that the.Wigner-Ville distribution is a more intuitive and reasonable nonlinear time-frequency distribution, and it can also describe the instantaneous power spectrum of the signal, but it is in the case of the instantaneous power spectrum of the signal. The false component produced by the convolution process causes the chaotic time frequency characteristics of the signal. The Fu Liye transform decomposes the signal into a series of sinusoidal waves of different frequency segments, and the wavelet transform decomposes the signal into its small wave basis. The vibration signal of the rolling bearing is nonlinear and non-stationary, and the above method is used to analyze the non-stationary and nonlinear signals. Hilbert Huang Bianhuan (HHT) is an adaptive time-frequency analysis method, including two parts of empirical mode decomposition (EMD) and Hilbert spectrum analysis (HAS)..EMD is decomposed into a finite single component signal based on the time scale characteristics of the signal itself, which is called the intrinsic mode function (IMF). This method is not the same as the intrinsic mode function (IMF). The analysis of linear nonstationary data has great potential, especially in the analysis of typical time-frequency energy negotiation signals. However, the endpoint effect of EMD, modal confusion, over envelope and under envelope, negative frequency, instantaneous frequency, and lack of a certain theoretical basis, still need further study.
Smith recently proposed a new adaptive time-frequency analysis method, Local Mean Decomposition. The principle of local mean mean decomposition (LMD).LMD is based on sliding average processing to decompose a complex multicomponent signal into a number of product function components (PF). The sliding step length (MA) used is important for the decomposition results. In addition, pure frequency modulation (FM). The standard of the signal will also affect the performance of the method. Compared with the EMD method, LMD has more advantages, such as fewer iterations, more stable endpoint effects and higher instantaneous frequency resolution. The local mean of the LMD decomposition is not enveloped and under enveloping, and the local amplitude of LMD is more than that of the EMD and the lower envelope. The local mean and local amplitude are all based on the local characteristic time scale of the signal.
Another adaptive time-frequency analysis method, Local Characteristic-Scale Decomposition, local feature scale decomposition (LCD), is studied in this paper. The signal can be decomposed into a number of intrinsic scale components (ISC) by LCD, and each ISC component contains local characteristics of the signal, so it can be extracted more accurately and effectively by this method. The performance of the original signal feature information.LCD is superior to EMD. in reducing the endpoint effect, the iteration time and the accuracy of the instantaneous characteristics. In addition, LCD can improve the marginal effect of the EMD decomposition signal. Based on the above advantages, this paper presents a fault diagnosis method for rolling bearings based on LMD and LCD.
The fault diagnosis of rolling bearing is actually a state recognition process. State recognition uses statistical learning theory, supervised and unsupervised classification. State recognition methods commonly used for state recognition are Artificial neural network, ANN^ and support vector machine (Support Vector Machine, SVM). The main disadvantage of ANN is that it is difficult to determine the network The structure and parameters of the collaterals need a large number of samples, and it is difficult to obtain a large number of samples in practice. In addition, the slow convergence speed also greatly increases the calculation time.SVM is an effective pattern recognition method based on the statistical learning theory and the structural risk optimal principle. It can not only solve the overfitting, local optimal, and slow convergence speed in the ANN. .SVM is widely used in pattern recognition and other fields for small samples. However, the parameter selection of SVM has a great influence on the classification effect, and the selection of parameters is actually an optimization process. Therefore, the optimization algorithm should be used for the parameter selection of SVM. Genetic algorithm (Genetic algorit) HM, GA) and particle swarm optimization (Particle swarm optimization, PSO) are applied to the optimization of SVM parameters. The.GA algorithm has the problems of slow convergence speed and easy to lose local optimal solution. In addition, GA can not solve specific optimization problems and the.PSO algorithm is easy to describe, easy to realize, fast convergence speed and so on. Potential, but there is a defect that can not effectively avoid premature convergence.
Recently, a new optimization algorithm based on the principle of chemical reaction has been proposed and proved to be superior to other optimization algorithms in many aspects. The idea of chemical reaction optimization algorithm comes from the occurrence of chemical reactions, imitating the microscopic movement of molecules in chemical reactions, and achieving the advantage of using the phenomenon of the lowest energy of the chemical reaction generation. There are two algorithms based on the chemical reaction principle: one is Chemical Reaction Optimization (CRO), and the CRO principle is based on the potential energy of the system. When the potential energy is reduced to a minimum, the reaction system will gradually reach the equilibrium state. Therefore, the potential energy is feasible as the objective function of the minimization problem; The other is the artificial reaction optimization algorithm (Artificial Chemical Reaction Optimization Algorithm, ACROA). Enthalpy and entropy can be used as the objective function (state function) for minimizing and maximizing the problem. The enthalpy depends on the chemical properties of the matter, the state of temperature and pressure, and the entropy is used to measure the randomness of the chemical system components or the disease.ACROA There is a parameter, initial reactivity, so this method is easy to use. CRO and ACROA are applied to SVM parameters optimization. The results show that the support vector machine based on chemical reaction optimization (Chemical Reaction Optimization-Support Vector Machine, CRO-SVM) and support vector machine based on artificial reaction optimization algorithm (Art) Ificial Chemical Reaction Optimization Algorithm-Support Vector Machine, ACROA-SVM) is superior to genetic algorithm based support vector machines (Genetic algorithm-Support vectormachine, GA-SVM) and support vector machines based on Particle Swarm Optimization in solving classification problems. SVM). On this basis, the paper applies local mean decomposition (Local mean decomposition, LMD) and local feature scale decomposition (Local characteristic-scale decomposition, LCD) to ACROA-SVM and CRO-SVM, in the fault diagnosis of rolling bearings.
The main work and innovation of this paper are as follows:
1. the two time frequency analysis methods of LMD and LCD are studied. The comparison of LMD, LCD and empirical mode decomposition (Empirical mode decomposition, EMD) method is carried out respectively. The analysis results of the simulation signal and the rolling bearing fault experimental signal show that the LMD and LCD are superior to the EMD method in calculating the efficiency of the end effect. The more sex.
2. the theoretical study of chemical reaction algorithm is carried out. The limitations of the two heuristic algorithms of GA and PSO are analyzed. The principle of CRO and ACROA and the chemical reaction process are expounded. The parameters of the CRO and ACROA algorithms are proposed. The comparison analysis of CRO and ACROA and the above heuristic algorithms is made, and the advantages and disadvantages of the CRO and ACROA algorithms are summarized.
3. a SVM parameter optimization method based on CRO and ACROA is proposed. In support vector machines, the tradeoff between generalization ability and minimum training error and minimum model complexity is determined by kernel parameters and regular constant C. The kernel function parameters define the nonlinear mapping between the input space and the output space. If these parameters are not selected correctly, the parameters are not selected correctly. The performance of SVM will be weakened. In this paper, CRO, ACROA is used to optimize the kernel parameters and regular constant C. The results show that CRO and ACROA have better performance in training speed and classification rate than GA and PSO, and can obtain optimal optimization results.
4. fault diagnosis of rolling bearing is carried out by combining CRO, ACROA-SVM and LMD, LCD and other methods.
(1) a fault diagnosis of rolling bearings is proposed based on the LCD energy entropy and the support vector machine (LCD-ACROA-SVM) designed by ACROA algorithm. Firstly, the vibration acceleration signal is decomposed into several intrinsic scale components, and then the concept of LCD energy entropy is proposed. Secondly, the energy characteristics are extracted from the intrinsic scale components including the main fault information. As the input vector of the support vector machine classifier, the ACROA-SVM classifier is proposed to identify the rolling bearing fault mode. The rolling bearing of the inner ring fault and the outer ring fault is analyzed. The results show that the ACROA-SVM based diagnosis method and the LCD method can be used to extract the different frequency band energy levels accurately and effectively. Bearing failure mode, the proposed method is much better than the empirical mode decomposition method, and saves time more.
(2) a rolling bearing fault diagnosis based on LMD and ACROA-SVM is proposed. First, the local mean decomposition method is used to decompose the original modulation vibration signals extracted from the rolling bearings into several PF components. Secondly, in the envelope spectrum of the PF components containing the main fault information, the amplitude of the amplitude of the different fault characteristics is at the frequency. The ratio is defined as the characteristic amplitude ratio. Finally, the characteristic amplitude ratio is used as the input of the ACROA-SVM classifier and the fault mode of the rolling bearing is identified. The result shows that the ACROA-SVM classifier combined with the LMD method can effectively improve the accuracy of fault diagnosis and consume less time.
(3) a new fault diagnosis of rolling bearing based on LCD and CRO-SVM is proposed, for short, LCD-CRO-SVM. first, using LCD method to decompose the original vibration signal of rolling bearing into the sum of several intrinsic scale components. Secondly, the amplitude ratio of different fault characteristic frequencies is calculated in the envelope spectrum of a series of intrinsic scale components. Some amplitude ratios are used as the input of the CRO-SVM classifier. The experimental results show that the CRO-SVM classifier combined with the LCD method can obtain higher classification accuracy and less time than the other methods.

【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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