本文選題：齒輪箱 + 故障診斷　； 參考：《中北大學(xué)》2011年碩士論文

【摘要】：齒輪作為機(jī)械設(shè)備的傳動(dòng)部分,擔(dān)負(fù)著傳遞動(dòng)力和運(yùn)動(dòng)的重要使命,齒輪故障不僅會(huì)損壞齒輪本身,還直接關(guān)系著整個(gè)設(shè)備的運(yùn)轉(zhuǎn),甚至危及到人身安全,所以對齒輪傳動(dòng)系統(tǒng)進(jìn)行狀態(tài)監(jiān)測及故障診斷具有重大意義。 遺傳算法是模仿自然界生物進(jìn)化機(jī)制發(fā)展起來的隨機(jī)全局搜索優(yōu)化方法,它可在搜索過程中能夠自動(dòng)獲取和積累有關(guān)搜索空間的知識,并且自適應(yīng)地控制搜索過程以求得最優(yōu)的方案,具有良好的全局收斂能力和收斂速度快、效率高的優(yōu)點(diǎn),還具有全局搜索性,能夠克服神經(jīng)網(wǎng)絡(luò)固有的局部極小值問題,結(jié)合利用神經(jīng)網(wǎng)絡(luò)的非線性逼近能力,可以實(shí)現(xiàn)優(yōu)勢互補(bǔ),同時(shí)避免各自缺點(diǎn)。 本文在深入研究齒輪箱的結(jié)構(gòu)與工作過程的基礎(chǔ)上,建立了13-4-5型的遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò)的齒輪箱故障診斷系統(tǒng),實(shí)驗(yàn)系統(tǒng)以齒輪箱為實(shí)驗(yàn)對象,將選好的傳感器安裝到已選測點(diǎn)上,通過測量到齒輪箱振動(dòng)信號對其進(jìn)行消噪處理、提取反映齒輪箱運(yùn)轉(zhuǎn)特征信息的特征值以體現(xiàn)齒輪箱運(yùn)轉(zhuǎn)的狀況,提出改進(jìn)遺傳算法適應(yīng)值的一種新方法,并將其應(yīng)用于遺傳算法優(yōu)化的BP網(wǎng)絡(luò)模型應(yīng)用于齒輪故障診斷,建立改進(jìn)后的遺傳算法優(yōu)化神經(jīng)網(wǎng)絡(luò)的兩種模型并且進(jìn)行仿真,通過一系列訓(xùn)練和測試,結(jié)果表明:通過采用兩種GA優(yōu)化算法分別在18步和44步達(dá)到收斂,傳統(tǒng)的未經(jīng)改良的遺傳算法需要71步,收斂速度分別提高了74.6%和38.0 %,極大的提高了診斷的可靠性和準(zhǔn)確性,本文最后與BP神經(jīng)網(wǎng)絡(luò)比較,更加驗(yàn)證了改進(jìn)后遺傳算法的優(yōu)越性。
[Abstract]:As the transmission part of mechanical equipment, gear takes on the important mission of transmitting power and motion. Gear failure will not only damage the gear itself, but also directly affect the operation of the whole equipment, and even endanger the personal safety. So the condition monitoring and fault diagnosis of gear transmission system is of great significance. Genetic algorithm (GA) is a stochastic global search optimization method which imitates the evolutionary mechanism of natural organisms. It can automatically acquire and accumulate knowledge about search space in the process of searching. It has the advantages of good global convergence ability, fast convergence speed, high efficiency, and global search ability, which can overcome the inherent local minimum problem of neural network. Combined with the nonlinear approximation ability of neural network, the advantages of the neural network can be complementary and their shortcomings can be avoided at the same time. On the basis of deeply studying the structure and working process of the gearbox, this paper establishes a gearbox fault diagnosis system based on the genetic algorithm optimization neural network of model 13-4-5. The experimental system takes the gearbox as the experimental object. The selected sensor is installed on the selected measuring point, the noise is removed by measuring the vibration signal of the gearbox, and the characteristic value reflecting the characteristic information of the gearbox operation is extracted to reflect the condition of the gearbox operation. A new method to improve the fitness of genetic algorithm is proposed. The BP neural network model optimized by genetic algorithm is applied to gear fault diagnosis, and two models of improved genetic algorithm to optimize neural network are established and simulated. Through a series of training and testing, the results show that the two GA optimization algorithms can converge at 18 and 44 steps respectively, and the traditional unimproved genetic algorithm needs 71 steps. The convergence rate is increased by 74.6% and 38.0% respectively, which greatly improves the reliability and accuracy of diagnosis. Finally, compared with BP neural network, the superiority of the improved genetic algorithm is verified.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 付煒;彭光劍;;基于小波閾值去噪的改進(jìn)方法[J];電子測量技術(shù);2006年06期

2 張杰;裴東興;李新娥;;存儲式石油射孔、壓裂測試系統(tǒng)的設(shè)計(jì)[J];電子測量技術(shù);2008年06期

3 熊軍,李鳳英,沈玉娣;基于高階倒譜熵的齒輪故障診斷[J];機(jī)械傳動(dòng);2005年02期

4 李輝;鄭海起;唐力偉;;基于倒階次譜分析的齒輪故障診斷研究[J];振動(dòng)與沖擊;2006年05期

5 賀恩華;朱利民;;自適應(yīng)中值濾波器及其應(yīng)用[J];振動(dòng)、測試與診斷;2006年01期

6 魏秀業(yè);潘宏俠;馬清峰;;粒子群優(yōu)化的神經(jīng)網(wǎng)絡(luò)在故障診斷中的應(yīng)用[J];振動(dòng)、測試與診斷;2006年02期

7 孫暉;朱善安;;基于自適應(yīng)濾波的滾動(dòng)軸承故障診斷研究[J];浙江大學(xué)學(xué)報(bào)(工學(xué)版);2005年11期

8 牛立勇,關(guān)惠玲;細(xì)化和倒譜分析在坦克齒輪箱故障診斷中的應(yīng)用[J];鄭州大學(xué)學(xué)報(bào)(工學(xué)版);2003年03期

相關(guān)碩士學(xué)位論文 前9條

1 劉景浩;齒輪傳動(dòng)故障診斷專家系統(tǒng)的研究與應(yīng)用[D];重慶大學(xué);2005年

2 黃錫泉;齒輪箱狀態(tài)監(jiān)測與故障診斷專家系統(tǒng)研究[D];中北大學(xué);2006年

3 姚曉睿;基于Web遠(yuǎn)程故障診斷系統(tǒng)的研究[D];西安科技大學(xué);2008年

4 蔣一然;基于遺傳神經(jīng)網(wǎng)絡(luò)的柴油機(jī)故障診斷技術(shù)研究[D];大連海事大學(xué);2009年

5 劉亞軍;基于小波分析和神經(jīng)網(wǎng)絡(luò)的電機(jī)故障診斷研究[D];中北大學(xué);2009年

6 周亮;基于小波分析的齒輪箱故障診斷技術(shù)的分析與研究[D];武漢科技大學(xué);2009年

7 盧衛(wèi)萍;基于小波神經(jīng)網(wǎng)絡(luò)的齒輪箱故障診斷研究[D];燕山大學(xué);2009年

8 王敏;基于蟻群優(yōu)化算法的齒輪箱故障診斷研究[D];中北大學(xué);2010年

9 孫黎明;基于粒子群優(yōu)化和系統(tǒng)特性的齒輪箱故障診斷研究[D];中北大學(xué);2010年

，

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