發(fā)布時(shí)間：2018-05-01 07:23

  本文選題：道岔智能故障診斷 + BP神經(jīng)網(wǎng)絡(luò)��； 參考：《蘭州交通大學(xué)》2011年碩士論文

【摘要】：隨著鐵路運(yùn)行速度的逐年攀升,較快的列車(chē)運(yùn)行速度對(duì)道岔提出了更加嚴(yán)格的要求。本論文是道岔監(jiān)測(cè)系統(tǒng)項(xiàng)目的一個(gè)子課題,是在道岔監(jiān)測(cè)系統(tǒng)提供的大量道岔狀態(tài)數(shù)據(jù)的基礎(chǔ)上,應(yīng)用神經(jīng)網(wǎng)絡(luò)對(duì)道岔進(jìn)行智能故障診斷。本文是對(duì)道岔智能故障診斷的一次初步嘗試,目的是給道岔監(jiān)測(cè)系統(tǒng)的故障診斷功能提供一種可行性實(shí)現(xiàn)方法。 論文首先介紹神經(jīng)網(wǎng)絡(luò)的定義和原理,并分別的從兩個(gè)典型神經(jīng)網(wǎng)絡(luò)——反向傳播神經(jīng)網(wǎng)絡(luò)(BP神經(jīng)網(wǎng)絡(luò))和徑向基神經(jīng)網(wǎng)絡(luò)(RBF神經(jīng)網(wǎng)絡(luò))兩方面詳細(xì)闡述了網(wǎng)絡(luò)的構(gòu)造,學(xué)習(xí)算法及其應(yīng)用。隨后,為了方便構(gòu)造神經(jīng)網(wǎng)絡(luò)系統(tǒng),在介紹道岔轉(zhuǎn)換系統(tǒng)結(jié)構(gòu)的基礎(chǔ)上,分析各種典型故障的機(jī)理,對(duì)雜亂無(wú)章的各種故障進(jìn)行了統(tǒng)一分類,并系統(tǒng)地介紹提供各種道岔監(jiān)測(cè)數(shù)據(jù)的道岔監(jiān)測(cè)系統(tǒng)。最后,運(yùn)用MATLAB分別構(gòu)造BP神經(jīng)網(wǎng)絡(luò)模型和RBF神經(jīng)網(wǎng)絡(luò)模型。多次測(cè)試后,對(duì)網(wǎng)絡(luò)性能進(jìn)行對(duì)比研究,發(fā)現(xiàn)診斷結(jié)果基本達(dá)到預(yù)期的診斷要求,完成了道岔智能故障診斷的理論研究。 本文重點(diǎn)在以下幾個(gè)方面進(jìn)行探索與研究： 按照道岔故障機(jī)理和神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)特性,把道岔故障分為三類,每類構(gòu)造一個(gè)子神經(jīng)網(wǎng)絡(luò),總體組建成一個(gè)并行神經(jīng)網(wǎng)絡(luò)系統(tǒng)框架。 選擇最優(yōu)BP算法。對(duì)某個(gè)子神經(jīng)網(wǎng)絡(luò)構(gòu)造BP神經(jīng)網(wǎng)絡(luò)模型,應(yīng)用多種常見(jiàn)BP算.法分別對(duì)網(wǎng)絡(luò)訓(xùn)練并測(cè)試,從測(cè)試結(jié)果中獲得每種BP算法的優(yōu)勢(shì)和劣勢(shì)。 設(shè)計(jì)基于BP算法的并行神經(jīng)網(wǎng)絡(luò)故障診斷模型。針對(duì)于每一個(gè)子神經(jīng)網(wǎng)絡(luò),利用經(jīng)驗(yàn)公式得出隱含層神經(jīng)元個(gè)數(shù)的最小范圍,然后在最小范圍內(nèi)對(duì)隱含層神經(jīng)元個(gè)數(shù)逐個(gè)嘗試,分析不同隱含層節(jié)點(diǎn)數(shù)對(duì)網(wǎng)絡(luò)性能的影響,采用Levenberg-Marquart算法構(gòu)造最優(yōu)BP神經(jīng)網(wǎng)絡(luò)。然后訓(xùn)練網(wǎng)絡(luò)并進(jìn)行故障診斷測(cè)試。 設(shè)計(jì)基于RBF算法的并行神經(jīng)網(wǎng)絡(luò)故障診斷模型。針對(duì)于每一個(gè)子神經(jīng)網(wǎng)絡(luò),通過(guò)多次試驗(yàn)獲取隱含層神經(jīng)元個(gè)數(shù)和徑向基分布密度的最優(yōu)值并構(gòu)造性能最佳的RBF神經(jīng)網(wǎng)絡(luò),然后訓(xùn)練網(wǎng)絡(luò)并進(jìn)行故障診斷測(cè)試。 通過(guò)一系列理論研究和大量仿真試驗(yàn)證明：神經(jīng)網(wǎng)絡(luò)技術(shù)運(yùn)用在道岔智能故障診斷方面是切實(shí)可行的。該方法能快速、有效地診斷出故障原因,為維修人員提供技術(shù)支持。
[Abstract]:With the increasing of railway running speed, the higher train speed puts forward more strict requirements for turnout. This paper is a sub-topic of the turnout monitoring system project, which is based on a large number of switch state data provided by the turnout monitoring system, and uses the neural network to diagnose the intelligent fault of the switch. This paper is a preliminary attempt for intelligent fault diagnosis of turnout, which aims to provide a feasible method for fault diagnosis of turnout monitoring system. Firstly, the definition and principle of neural network are introduced, and the structure of neural network is described in detail from two aspects: back propagation neural network (BP) and radial basis function neural network (RBF). Learning algorithm and its application. Then, in order to facilitate the construction of neural network system, on the basis of introducing the structure of switch switching system, the mechanism of various typical faults is analyzed, and the disorderly faults are classified uniformly. The turnout monitoring system which provides all kinds of turnout monitoring data is introduced systematically. Finally, BP neural network model and RBF neural network model are constructed by MATLAB. After many tests, the performance of the network is compared, and it is found that the diagnosis results basically meet the expected diagnostic requirements, and the theoretical research on intelligent fault diagnosis of switch is completed. This paper focuses on the following aspects of exploration and research: According to the fault mechanism of switch and the characteristics of neural network structure, the switch faults are divided into three types, each of which is composed of a sub-neural network and a parallel neural network system framework. The optimal BP algorithm is selected. The BP neural network model is constructed by a subneural network, and many common BP calculations are applied. The advantages and disadvantages of each BP algorithm are obtained from the test results. A parallel neural network fault diagnosis model based on BP algorithm is designed. For each sub-neural network, the minimum range of the number of neurons in the hidden layer is obtained by empirical formula, and then the number of neurons in the hidden layer is tried one by one in the minimum range to analyze the effect of the number of hidden layer nodes on the performance of the network. The optimal BP neural network is constructed by Levenberg-Marquart algorithm. The network is then trained and tested for fault diagnosis. A parallel neural network fault diagnosis model based on RBF algorithm is designed. For each sub-neural network, the optimal values of the number of hidden layer neurons and the radial basis distribution density are obtained through several experiments, and the RBF neural network with the best performance is constructed, and then the network is trained and tested for fault diagnosis. Through a series of theoretical research and a large number of simulation experiments, it is proved that the application of neural network technology in intelligent fault diagnosis of turnout is feasible. This method can quickly and effectively diagnose the fault cause and provide technical support for maintenance personnel.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3;TP183

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 王彥快;25Hz相敏軌道電路分路不良預(yù)警系統(tǒng)的研究與設(shè)計(jì)[D];蘭州交通大學(xué);2013年

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本文編號(hào)：1828323