本文關(guān)鍵詞： 提升機(jī) 遠(yuǎn)程監(jiān)測(cè) 故障診斷 組態(tài)王 支持向量機(jī) 試驗(yàn)系統(tǒng)　出處：《太原理工大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：提升設(shè)備是能源開(kāi)采業(yè)中重要的機(jī)械設(shè)備,作為井上與井下的連接樞紐,其安全穩(wěn)定運(yùn)行的重要性不言而喻,所以對(duì)于研究保證提升設(shè)備的安全穩(wěn)定工作具有重要的意義。本文將礦井提升機(jī)作為提升設(shè)備研究的工程背景,重點(diǎn)探討了提升機(jī)遠(yuǎn)程監(jiān)測(cè)與故障診斷的研究方向,并結(jié)合目前有關(guān)提升機(jī)試驗(yàn)系統(tǒng)研究情況,提出了提升機(jī)遠(yuǎn)程監(jiān)測(cè)與故障診斷“現(xiàn)場(chǎng)試驗(yàn)子系統(tǒng)+互聯(lián)網(wǎng)試驗(yàn)子系統(tǒng)”的試驗(yàn)系統(tǒng)搭建模式。本文針對(duì)現(xiàn)場(chǎng)試驗(yàn)子系統(tǒng),提出了基于組態(tài)王的現(xiàn)場(chǎng)監(jiān)測(cè)與診斷模式,包括監(jiān)測(cè)內(nèi)容設(shè)計(jì),硬件的選型設(shè)計(jì),硬件的連接設(shè)計(jì),采集方案的設(shè)計(jì),通訊方式設(shè)計(jì)以及現(xiàn)場(chǎng)監(jiān)測(cè)系統(tǒng)方案設(shè)計(jì),同時(shí)還有現(xiàn)場(chǎng)數(shù)據(jù)庫(kù)的建立。針對(duì)以往提升機(jī)的監(jiān)測(cè)系統(tǒng)模擬量采集匱乏導(dǎo)致其他應(yīng)用研究缺乏數(shù)據(jù),同時(shí)用戶維護(hù)開(kāi)發(fā)難度大,提出了系統(tǒng)模塊化設(shè)計(jì)并且將更多的模擬量納入采集范圍。本文針對(duì)互聯(lián)網(wǎng)試驗(yàn)子系統(tǒng),提出了基于互聯(lián)網(wǎng)的遠(yuǎn)程監(jiān)測(cè)與診斷模式,包括Web服務(wù)器以及本地服務(wù)器的硬件選型設(shè)計(jì),遠(yuǎn)程數(shù)據(jù)傳輸方式設(shè)計(jì)以及遠(yuǎn)程數(shù)據(jù)庫(kù)的建立,基于ASP.NET的互聯(lián)網(wǎng)監(jiān)測(cè)系統(tǒng)設(shè)計(jì)。針對(duì)以往數(shù)據(jù)不能實(shí)時(shí)顯示在網(wǎng)頁(yè)監(jiān)測(cè)與診斷頁(yè)面上,提出了同步數(shù)據(jù)庫(kù)系統(tǒng)設(shè)計(jì),并將數(shù)據(jù)實(shí)時(shí)傳輸?shù)交ヂ?lián)網(wǎng)監(jiān)測(cè)界面上進(jìn)行顯示。本文針對(duì)以往提升機(jī)遠(yuǎn)程監(jiān)測(cè)與診斷系統(tǒng)中故障診斷不足,且故障樣本數(shù)據(jù)少的情況,提出了支持向量機(jī)的故障診斷方法,為了解決支持向量機(jī)參數(shù)選擇困難和其對(duì)于故障診斷的影響,提出利用粒子群優(yōu)化算法(PSO),遺傳算法(GA)對(duì)支持向量機(jī)的參數(shù)進(jìn)行優(yōu)化,提高提升機(jī)故障診斷分類(lèi)的準(zhǔn)確率。試驗(yàn)證明:“現(xiàn)場(chǎng)試驗(yàn)子系統(tǒng)+互聯(lián)網(wǎng)試驗(yàn)子系統(tǒng)”這種提升設(shè)備遠(yuǎn)程監(jiān)測(cè)與故障診斷試驗(yàn)系統(tǒng)模式不僅能夠在多層次上對(duì)提升設(shè)備進(jìn)行監(jiān)測(cè),診斷以及維護(hù),能夠?qū)崿F(xiàn)現(xiàn)場(chǎng)數(shù)據(jù)庫(kù)與遠(yuǎn)程數(shù)據(jù)庫(kù)數(shù)據(jù)同步,并且故障診斷用戶圖形界面簡(jiǎn)潔,操作簡(jiǎn)單。參數(shù)優(yōu)化后的支持向量機(jī)故障診斷方法具有快速,高診斷率,所需樣本少的優(yōu)點(diǎn),使得該遠(yuǎn)程監(jiān)測(cè)與診斷系統(tǒng)故障分類(lèi)更高,實(shí)現(xiàn)動(dòng)態(tài)診斷,并且可以進(jìn)行運(yùn)動(dòng)學(xué),結(jié)構(gòu)靜力學(xué),電控系統(tǒng),加載系統(tǒng)等其它科學(xué)應(yīng)用研究試驗(yàn)。
[Abstract]:Lifting equipment is an important equipment of energy exploitation, as well as the connection hub, it is self-evident importance of the safe and stable operation, so the research has important significance to enhance the security and stability of equipment. This paper will study the engineering background as lifting equipment of mine hoist, discusses the research direction of hoist remote monitoring and fault diagnosis, combined with the current research situation about hoist test system, put forward the test system of the elevator remote monitoring and fault diagnosis "field test subsystem + Internet test system" construction mode. According to the field test subsystem, proposed on-site monitoring and diagnosis model based on Kingview, including monitoring content design, selection of hardware design, hardware connection design, collection design, communication design and field monitoring system The system design, as well as the scene database. In the past, enhance the monitoring system of analog acquisition led to the lack of other applications of lack of data, and user maintenance is difficult to develop and put forward the modular design of the system and the analog more into the collection range. According to the Internet test subsystem, the remote monitoring and the diagnosis model based on Internet, including Web servers and hardware selection and design of the local server, the design of remote data transmission and the establishment of the remote database, the design of network monitoring system based on ASP.NET. According to the data of the past can not be displayed in real-time monitoring and diagnosis of web page, put forward the design of synchronous database system, and real-time data transmission to the Internet monitoring interface for display. In this paper the elevator remote monitoring and diagnosis system fault The diagnosis is insufficient, and the failure of few sample data, the paper puts forward a fault diagnosis method of support vector machine, support vector machine in order to solve the difficulty in parameter selection and its effect on the fault diagnosis, proposed using particle swarm optimization algorithm (PSO), genetic algorithm (GA) to optimize the parameters of SVM, improve the accuracy of hoist fault diagnosis classification. Experimental results show that: "field test subsystem + Internet test system" the mode of remote monitoring and fault diagnosis of equipment test system can not only at multiple levels of lifting equipment for monitoring, diagnosis and maintenance, to achieve on-site and remote databases and data synchronization, fault diagnosis of graphical user the interface is simple and easy to operate. The optimized parameters of support vector machine fault diagnosis method is rapid, high diagnostic rate, and required fewer samples, so that the remote monitoring The test and diagnosis system has higher fault classification and dynamic diagnosis, and can carry out other scientific application research experiments such as kinematics, structural statics, electronic control system, loading system and so on.

【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP274;TD534

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