本文選題：粒子群優(yōu)化算法 + 小波理論　； 參考：《武漢工程大學(xué)》2013年碩士論文

【摘要】：粒子群優(yōu)化算法(Particle Swarm Optimization，簡(jiǎn)稱(chēng)PSO)是一種基于群體智能的優(yōu)化方法。算法主要利用生物群體內(nèi)個(gè)體的協(xié)作與競(jìng)爭(zhēng)等復(fù)雜的行為產(chǎn)生群體智能，并為工程優(yōu)化問(wèn)題提供高效的解決方法。在優(yōu)化過(guò)程中，，首先將工程問(wèn)題轉(zhuǎn)化為簡(jiǎn)單的數(shù)學(xué)模型，充分了解該數(shù)學(xué)模型的具體涵義以及模型中各參數(shù)的意義和取值范圍；然后對(duì)粒子群優(yōu)化算法所涉及的參數(shù)進(jìn)行初始化，提出基于該工程問(wèn)題的適應(yīng)度函數(shù)；最后根據(jù)適應(yīng)度函數(shù)的精度評(píng)價(jià)優(yōu)化參數(shù)，直至滿(mǎn)足適應(yīng)度函數(shù)的精度。與傳統(tǒng)優(yōu)化算法相比，粒子群優(yōu)化算法在多維函數(shù)尋優(yōu)方面有著算法簡(jiǎn)單，參數(shù)少，收斂速度快等優(yōu)點(diǎn)，故在工程實(shí)踐中有著廣泛的應(yīng)用。本文在研究粒子群優(yōu)化算法理論的基礎(chǔ)上，結(jié)合小波理論，將這種算法應(yīng)用于齒輪箱的故障信號(hào)識(shí)別中。 針對(duì)齒輪箱故障信號(hào)所表現(xiàn)出的非線性和非平穩(wěn)性，本文運(yùn)用自適應(yīng)Morlet小波建立振動(dòng)信號(hào)的數(shù)學(xué)模型，旨在提取故障信號(hào)的特征信息。自適應(yīng)小波變換(Adaptive Wavelet Transform，簡(jiǎn)稱(chēng)AWT)確保粒子群算法所優(yōu)化的模型參數(shù)和自適應(yīng)小波函數(shù)的參數(shù)是一一對(duì)應(yīng)的，為高精度的時(shí)頻分析提供了理論依據(jù)。 基于對(duì)粒子群優(yōu)化算法和小波理論的應(yīng)用研究，本文首先對(duì)原始信號(hào)進(jìn)行希爾伯特變換，獲得信號(hào)包絡(luò)。再利用Morlet小波函數(shù)建立信號(hào)包絡(luò)的數(shù)學(xué)模型，確定待優(yōu)參數(shù)。然后運(yùn)用本文提出的粒子群優(yōu)化算法和最小均方誤差(LeastMean Square Error，簡(jiǎn)稱(chēng)LMSE)求出數(shù)學(xué)模型的參數(shù)并優(yōu)化，將所得參數(shù)反代入信號(hào)模型中得到模型信號(hào)。最后使用自適應(yīng)連續(xù)小波變換對(duì)齒輪箱的故障狀態(tài)進(jìn)行診斷評(píng)估。結(jié)果表明基于粒子群算法和小波理論的方法對(duì)齒輪箱的故障診斷是有效的。 本文集成了粒子群優(yōu)化算法的參數(shù)優(yōu)化效用和小波理論的時(shí)頻分辨能力，提出了基于粒子群算法和小波理論的機(jī)械故障診斷方法，它能很好地對(duì)五種齒輪的損壞程度進(jìn)行識(shí)別。通過(guò)對(duì)同一種裂紋故障類(lèi)別下的不同齒輪損壞程度進(jìn)行識(shí)別表明所提出的故障診斷方法是有效且可靠的。研究結(jié)果同時(shí)也對(duì)粒子群優(yōu)化算法和小波理論在其他領(lǐng)域的應(yīng)用提供了一種新的思路。
[Abstract]:Particle Swarm Optimization (PSO) is an optimization method based on swarm intelligence. The algorithm mainly uses complex behaviors such as cooperation and competition among individuals in biological communities to produce swarm intelligence and provides efficient solutions to engineering optimization problems. In the process of optimization, the engineering problem is first transformed into a simple mathematical model, and the specific meaning of the mathematical model and the significance and value range of the parameters in the model are fully understood. Then the parameters involved in the PSO algorithm are initialized and the fitness function based on the engineering problem is proposed. Finally the optimization parameters are evaluated according to the accuracy of the fitness function until the accuracy of the fitness function is satisfied. Compared with the traditional optimization algorithm, particle swarm optimization algorithm has the advantages of simple algorithm, few parameters and fast convergence speed in multi-dimensional function optimization, so it is widely used in engineering practice. Based on the theory of particle swarm optimization and wavelet theory, this paper applies this algorithm to the fault signal identification of gearbox. Aiming at the nonlinearity and nonstationarity of gearbox fault signal, this paper uses adaptive Morlet wavelet to establish the mathematical model of vibration signal, aiming at extracting the characteristic information of fault signal. Adaptive wavelet transform (AWT) ensures that the model parameters optimized by particle swarm optimization and the parameters of adaptive wavelet function are one-to-one corresponding, which provides a theoretical basis for high-precision time-frequency analysis. Based on the application of particle swarm optimization algorithm and wavelet theory, the original signal is firstly transformed by Hilbert transform, and the envelope of the signal is obtained. Then the mathematical model of signal envelope is established by using Morlet wavelet function to determine the optimal parameters. Then, using the particle swarm optimization algorithm and the least mean square error (LMSE) proposed in this paper, the parameters of the mathematical model are obtained and optimized, and the parameters are reversed into the signal model to get the model signal. Finally, adaptive continuous wavelet transform is used to diagnose and evaluate the fault state of gearbox. The results show that the method based on particle swarm optimization and wavelet theory is effective for gearbox fault diagnosis. In this paper, the parameter optimization utility of particle swarm optimization algorithm and the time-frequency resolution of wavelet theory are integrated, and a mechanical fault diagnosis method based on particle swarm optimization and wavelet theory is proposed, which can identify the damage degree of five gears well. It is proved that the proposed fault diagnosis method is effective and reliable by identifying the different gear damage degree under the same crack fault category. The results also provide a new idea for the application of particle swarm optimization and wavelet theory in other fields.
【學(xué)位授予單位】：武漢工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TH165.3;TP18

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