本文選題：工業(yè)以太網(wǎng) + 智能算法��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：近些年來(lái),隨著生產(chǎn)制造企業(yè)網(wǎng)絡(luò)規(guī)模的不斷擴(kuò)大,對(duì)網(wǎng)絡(luò)的管理變得越來(lái)越困難。加上工業(yè)網(wǎng)絡(luò)所處的特殊的環(huán)境,很容易造成設(shè)備故障,從而給企業(yè)帶來(lái)經(jīng)濟(jì)損失甚至是人員傷亡。如何對(duì)網(wǎng)絡(luò)進(jìn)行有效的管理,是當(dāng)前網(wǎng)絡(luò)安全領(lǐng)域研究的重點(diǎn)。網(wǎng)絡(luò)故障診斷與健康評(píng)估技術(shù)是根據(jù)系統(tǒng)當(dāng)前及歷史數(shù)據(jù)判斷故障類型及評(píng)估系統(tǒng)當(dāng)前健康程度的智能化技術(shù),通過(guò)對(duì)系統(tǒng)進(jìn)行建模及分析,可以盡快找到產(chǎn)生故障的原因以及從整體上把握網(wǎng)絡(luò)的健康狀況,從而給維修人員提供決策指導(dǎo)。本文在國(guó)家863項(xiàng)目的支撐下,重點(diǎn)研究了工業(yè)以太網(wǎng)絡(luò)故障診斷及健康評(píng)估技術(shù)。主要研究?jī)?nèi)容如下:一是設(shè)計(jì)了綜合監(jiān)控管理平臺(tái),該平臺(tái)負(fù)責(zé)設(shè)備狀態(tài)數(shù)據(jù)采集,平臺(tái)的優(yōu)勢(shì)是通過(guò)在管理端駐留插件的方式采集各設(shè)備的參數(shù)信息,使得對(duì)設(shè)備的管理更加靈活。二是根據(jù)生產(chǎn)制造企業(yè)網(wǎng)絡(luò)的數(shù)據(jù)特點(diǎn),提出了一種基于隨機(jī)森林的智能故障診斷算法(CSRF)。該算法從樣本采樣和模型組合兩方面對(duì)隨機(jī)森林進(jìn)行改進(jìn)。前者使用分類采樣技術(shù)為每個(gè)基本分類器單獨(dú)生成訓(xùn)練樣本,緩解了采樣偏置和數(shù)據(jù)不均衡帶來(lái)的問(wèn)題。后者綜合考慮了基本分類器的投票數(shù)和置信度兩方面因素,提高了診斷的準(zhǔn)確率。三是針對(duì)目前網(wǎng)絡(luò)健康評(píng)估技術(shù)存在的問(wèn)題,提出了一種多神經(jīng)網(wǎng)絡(luò)融合的健康評(píng)估算法(MNN)。該算法充分考慮到網(wǎng)絡(luò)的單點(diǎn)特征以及鏈路特征,使用卷積網(wǎng)絡(luò)和BP網(wǎng)絡(luò)對(duì)不同緯度的特征進(jìn)行建模分析,從而評(píng)估網(wǎng)絡(luò)健康狀況。四是通過(guò)實(shí)驗(yàn)設(shè)計(jì)及結(jié)果分析,驗(yàn)證了本文提出的算法的有效性。
[Abstract]:In recent years, with the continuous expansion of manufacturing enterprises network, network management becomes more and more difficult. Combined with the special environment of industrial network, it is easy to cause equipment failure, which brings economic losses and even casualties to enterprises. How to manage the network effectively is the focus of the research in the field of network security. The network fault diagnosis and health assessment technology is an intelligent technology to judge the fault type and assess the current health degree of the system according to the current and historical data of the system. Through the modeling and analysis of the system, the network fault diagnosis and health assessment technology is introduced. It can find out the cause of the failure and grasp the health condition of the network as a whole, so as to provide decision guidance for the maintainers. Supported by the National 863 Project, this paper focuses on the industrial Ethernet network fault diagnosis and health assessment technology. The main research contents are as follows: first, a comprehensive monitoring and management platform is designed, which is responsible for the state data collection of the equipment. The advantage of the platform is to collect the parameter information of each device by the way of staying in the management terminal. Make the management of equipment more flexible. Secondly, according to the data characteristics of manufacturing enterprise network, an intelligent fault diagnosis algorithm based on random forest is proposed. The algorithm improves the random forest from two aspects: sample sampling and model combination. The former uses classification sampling technique to generate training samples for each basic classifier separately, which alleviates the problems caused by sampling bias and data imbalance. The latter improves the accuracy of diagnosis by considering the voting number and confidence of the basic classifier. Thirdly, aiming at the problems existing in the current network health assessment technology, a multi-neural network fusion health assessment algorithm is proposed. Considering the single point feature and link feature of the network, the algorithm uses convolutional network and BP network to model and analyze the characteristics of different latitudes, so as to evaluate the health status of the network. Fourth, the effectiveness of the proposed algorithm is verified by experimental design and result analysis.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP393.06

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