本文選題：軌道交通車輛 + 軸承故障診斷�。� 參考：《南京理工大學(xué)》2013年碩士論文

【摘要】：滾動(dòng)軸承作為軌道車輛走行系的最重要部件之一,其運(yùn)行狀態(tài)對(duì)于保障車輛的行駛安全具有重要意義,因此對(duì)軌道車輛滾動(dòng)軸承的故障進(jìn)行準(zhǔn)確、高效的診斷是一個(gè)亟需解決的問題。本文在總結(jié)和吸取前人研究成果的基礎(chǔ)上,提出將經(jīng)驗(yàn)?zāi)B(tài)分解與遺傳算法優(yōu)化的RBF神經(jīng)網(wǎng)絡(luò)相結(jié)合實(shí)現(xiàn)軌道車輛滾動(dòng)軸承的故障診斷。 首先,介紹了滾動(dòng)軸承故障診斷的機(jī)理、故障形式及產(chǎn)生原因和振動(dòng)模型,并討論了故障特征信息提取的常用方法和各自的特點(diǎn)。重點(diǎn)對(duì)小波包分析和EMD方法進(jìn)行了實(shí)例仿真,證明了小波包分析和EMD可以有效用于軸承故障特征信息的提取。 其次,在故障模式識(shí)別上,采用基于神經(jīng)網(wǎng)絡(luò)的故障模式識(shí)別方法,選擇BP和RBF兩種典型神經(jīng)網(wǎng)絡(luò),結(jié)合小波包和EMD兩種特征提取方法,分別建立了小波包-BP、小波包-RBF、EMD-BP、EMD-RBF四種故障診斷模型。利用Benchmark數(shù)據(jù)對(duì)各模型進(jìn)行仿真實(shí)驗(yàn),結(jié)果證明EMD在故障特征提取上相比小波包有優(yōu)勢,RBF神經(jīng)網(wǎng)絡(luò)比BP神經(jīng)網(wǎng)絡(luò)有更好的故障識(shí)別性能。 再次,提出采用遺傳算法優(yōu)化RBF神經(jīng)網(wǎng)絡(luò)參數(shù),進(jìn)一步提升RBF神經(jīng)網(wǎng)絡(luò)的故障識(shí)別性能,結(jié)合小波包和EMD分別建立小波包-GA-RBF和EMD-GA-RBF兩種故障診斷模型。利用Benchmark數(shù)據(jù)對(duì)各模型進(jìn)行仿真實(shí)驗(yàn),結(jié)果證明基于遺傳算法優(yōu)化的RBF神經(jīng)網(wǎng)絡(luò)在故障識(shí)別精度上有了很大提高。 最后,以上述研究分析為基礎(chǔ),采用小波包-GA-RBF和EMD-GA-RBF兩種軸承故障診斷模型,利用實(shí)測的軌道車輛滾動(dòng)軸承故障數(shù)據(jù)進(jìn)行仿真實(shí)驗(yàn),結(jié)果證明本文提出的基于EMD分解結(jié)合遺傳算法優(yōu)化的RBF神經(jīng)網(wǎng)絡(luò)可以用于軌道車輛的軸承故障診斷。
[Abstract]:Rolling bearing is one of the most important parts of rail vehicle running system. Its running state is of great significance to ensure the safety of vehicle, so the fault of rolling bearing of rail vehicle is accurate. Efficient diagnosis is an urgent problem to be solved. On the basis of summing up and absorbing the previous research results, this paper proposes to realize the fault diagnosis of rolling bearing of rail vehicle by combining empirical mode decomposition with RBF neural network optimized by genetic algorithm. Firstly, the fault diagnosis mechanism, fault form, causes and vibration model of rolling bearing are introduced, and the common methods of fault feature information extraction and their respective characteristics are discussed. The simulation results of wavelet packet analysis and EMD method show that wavelet packet analysis and EMD can be used to extract bearing fault feature information effectively. Secondly, in fault pattern recognition, the neural network-based fault pattern recognition method is adopted, two typical neural networks BP and RBF are selected, and two feature extraction methods, wavelet packet and EMD, are combined. Four kinds of fault diagnosis models, wavelet packet -BP and wavelet packet RBFN EMD-BPU EMD-RBF, are established respectively. The simulation results of each model using Benchmark data show that EMD has better fault identification performance than wavelet packet in fault feature extraction. Thirdly, genetic algorithm is used to optimize the parameters of RBF neural network to further improve the fault identification performance of RBF neural network. Combined with wavelet packet and EMD, two fault diagnosis models of wavelet packet -GA-RBF and EMD-GA-RBF are established respectively. The simulation results of each model based on Benchmark data show that the RBF neural network based on genetic algorithm has greatly improved the accuracy of fault identification. Finally, based on the above research and analysis, two kinds of bearing fault diagnosis models, wavelet packet -GA-RBF and EMD-GA-RBF, are used to simulate the rolling bearing fault data of rail vehicle. The results show that the proposed RBF neural network based on EMD decomposition and genetic algorithm can be used for bearing fault diagnosis of rail vehicles.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：U279.3;TH165.3;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉樂平;林鳳濤;;基于小波包特征向量與神經(jīng)網(wǎng)絡(luò)的滾動(dòng)軸承故障診斷[J];軸承;2008年04期

2 唐德堯,王定曉,楊政明,宋辛輝,王巍松;共振解調(diào)技術(shù)與機(jī)車車輛傳動(dòng)裝置故障診斷[J];電力機(jī)車技術(shù);2002年05期

3 張明君;張化光;;遺傳算法優(yōu)化的RBF神經(jīng)網(wǎng)絡(luò)控制器[J];電機(jī)與控制學(xué)報(bào);2007年02期

4 王本永,郭仁寧,李文生;基于小波包分析和BP網(wǎng)絡(luò)識(shí)別的齒輪故障診斷[J];遼寧工程技術(shù)大學(xué)學(xué)報(bào);2002年05期

5 于德介,程軍圣;EMD方法在齒輪故障診斷中的應(yīng)用[J];湖南大學(xué)學(xué)報(bào)(自然科學(xué)版);2002年06期

6 趙志剛;單曉虹;;一種基于遺傳算法的RBF神經(jīng)網(wǎng)絡(luò)優(yōu)化方法[J];計(jì)算機(jī)工程;2007年06期

7 史晉芳;;基于專家系統(tǒng)的數(shù)控機(jī)床故障診斷技術(shù)研究[J];機(jī)械設(shè)計(jì)與制造;2006年07期

8 王光研;許寶杰;;RBF神經(jīng)網(wǎng)絡(luò)在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用[J];機(jī)械設(shè)計(jì)與制造;2008年09期

9 劉宗政;陳懇;陳振華;陳雁;黃元林;;滾動(dòng)軸承的振動(dòng)特性分析及典型故障診斷[J];機(jī)械設(shè)計(jì)與制造;2009年03期

10 胡勁松;楊世錫;;基于自相關(guān)的旋轉(zhuǎn)機(jī)械振動(dòng)信號(hào)EMD分解方法研究[J];機(jī)械強(qiáng)度;2007年03期

相關(guān)博士學(xué)位論文 前1條

1 程軍圣;基于Hilbert-Huang變換的旋轉(zhuǎn)機(jī)械故障診斷方法研究[D];湖南大學(xué);2005年

相關(guān)碩士學(xué)位論文 前3條

1 魯新義;基于共振解調(diào)技術(shù)和小波分析的故障診斷[D];武漢科技大學(xué);2007年

2 曾蕓;基于小波分析的滾動(dòng)軸承故障診斷方法研究[D];南昌大學(xué);2007年

3 劉占濤;大型裝備系統(tǒng)狀態(tài)監(jiān)測及故障診斷集成方法研究[D];北京化工大學(xué);2009年

，

本文編號(hào)：1929494