本文選題：TRT + 神經(jīng)網(wǎng)絡(luò)��； 參考：《上海交通大學(xué)》2012年碩士論文

【摘要】：機(jī)械設(shè)備是企業(yè)生產(chǎn)的物質(zhì)基礎(chǔ),是生產(chǎn)力的重要組成部分。在工業(yè)生產(chǎn)中,隨著時(shí)間的推移,各種設(shè)備必然會(huì)產(chǎn)生各種形式的磨損,以及導(dǎo)致設(shè)備精度和效率的降低,從而使產(chǎn)品質(zhì)量下降,嚴(yán)重的還會(huì)造成設(shè)備事故,為此開展對(duì)設(shè)備故障及性能診斷技術(shù)的課題研究意義重大。 故障及性能診斷是一門快速發(fā)展的交叉學(xué)科,它集測(cè)試技術(shù)、軟件工程、計(jì)算機(jī)技術(shù)、信號(hào)處理、模式識(shí)別、人工智能、決策科學(xué)、信息科學(xué)等眾多現(xiàn)代科學(xué)技術(shù)于一體,成為既注重理論研究,又重視實(shí)際應(yīng)用的現(xiàn)代工程科學(xué),并逐步形成一個(gè)體系完整、理論嚴(yán)謹(jǐn)且具有重大工程意義的新學(xué)科。從當(dāng)前我國(guó)大多數(shù)企業(yè)對(duì)設(shè)備故障的處理體制來(lái)看,基本上都采用了習(xí)慣的“定期維修”和“事后維修”兩種方式。定期維修雖能發(fā)現(xiàn)一些早期故障,防止一部分突發(fā)事故的發(fā)生,但會(huì)因?yàn)閷?duì)不需要維護(hù)的設(shè)備過(guò)頻更換零部件而造成過(guò)剩維修,形成一些不必要的浪費(fèi)。對(duì)于事后維修而言,任何異常工況和突發(fā)故障導(dǎo)致的停機(jī)檢修和生產(chǎn)節(jié)拍的停頓,都必然造成生產(chǎn)工序的積壓,嚴(yán)重地影響生產(chǎn)計(jì)劃的順利完成。因此需要在生產(chǎn)過(guò)程中及早地對(duì)設(shè)備進(jìn)行故障診斷,做到“先人一步發(fā)現(xiàn)故障,先故障之前消除隱患”。 本課題以萊蕪鋼鐵股份公司能源動(dòng)力廠5#高爐煤氣余熱余壓能量回收透平發(fā)電裝置(Blast-Furnace Top pressure Recovery Turbine Unit,簡(jiǎn)稱TRT)的工況監(jiān)測(cè)與故障診斷為研究?jī)?nèi)容,研究的目的是要根據(jù)TRT在各種工況下表現(xiàn)出來(lái)的振動(dòng)、噪聲、溫度、液壓、轉(zhuǎn)子、轉(zhuǎn)速、氣味、泄露等所有規(guī)律特征信息去綜合分析和識(shí)別設(shè)備工作狀態(tài)、故障類型和故障的嚴(yán)重程度,最終得到對(duì)修復(fù)故障有重要指導(dǎo)作用的診斷結(jié)論。本文在分析TRT常見的故障機(jī)理基礎(chǔ)上,深入研究了神經(jīng)網(wǎng)絡(luò)技術(shù)的原理方法和應(yīng)用技術(shù)特點(diǎn),結(jié)合故障特點(diǎn)找出與其相對(duì)應(yīng)的特征量,構(gòu)建了TRT故障診斷系統(tǒng)的神經(jīng)網(wǎng)絡(luò)模型,并針對(duì)TRT透平轉(zhuǎn)子故障樣本進(jìn)行了神經(jīng)網(wǎng)絡(luò)訓(xùn)練,基于VB軟件實(shí)現(xiàn)了TRT系統(tǒng)的故障診斷界面開發(fā)。本文應(yīng)用故障診斷技術(shù)實(shí)現(xiàn)了對(duì)機(jī)組的保護(hù),避免了因高爐頂壓瞬間增大而導(dǎo)致的不必要停車,為保證高爐TRT長(zhǎng)期安全、穩(wěn)定、順行以及提高經(jīng)濟(jì)效益等提供了有力支持。
[Abstract]:Mechanical equipment is the material basis of enterprise production and an important part of productivity. In industrial production, with the passage of time, various kinds of equipment will inevitably produce various forms of wear and tear, and will lead to the reduction of equipment precision and efficiency, thus leading to a decline in product quality and serious equipment accidents. Therefore, it is of great significance to research the technology of equipment fault and performance diagnosis. Fault and performance diagnosis is a rapidly developing interdisciplinary subject. It integrates testing technology, software engineering, computer technology, signal processing, pattern recognition, artificial intelligence, decision science, information science and so on. It has become a modern engineering science which pays attention to both theoretical research and practical application, and gradually forms a new discipline with complete system, rigorous theory and great engineering significance. According to the current system of handling equipment failures in most enterprises in our country, two methods of "regular maintenance" and "afterwards maintenance" are basically adopted. Although periodic maintenance can find some early faults and prevent some sudden accidents from happening, it will cause excessive maintenance because of the excessive replacement of parts and components for the equipment that does not need maintenance, resulting in some unnecessary waste. For the maintenance after the event, any abnormal working conditions and sudden failures will inevitably cause the backlog of production procedures, which will seriously affect the smooth completion of production planning. Therefore, it is necessary to diagnose the equipment as early as possible in the process of production, so as to "find the fault first and eliminate the hidden trouble before the failure". In this paper, the monitoring and fault diagnosis of blast furnace gas residual heat residual pressure recovery turbine generator unit, Blast-Furnace Top pressure Recovery Turbine Unit, is taken as the research content in Laiwu Iron and Steel Co., Ltd. The purpose of the study is to comprehensively analyze and identify the working state of the equipment according to the characteristic information of vibration, noise, temperature, hydraulic pressure, rotor, speed, smell, leakage and so on, which are shown by TRT under various working conditions. Finally, the fault type and the severity of the fault can be used as an important guide for fault diagnosis. On the basis of analyzing the common fault mechanism of TRT, this paper deeply studies the principle, method and application technical characteristics of neural network technology, finds out the corresponding characteristic quantity according to the fault characteristic, and constructs the neural network model of TRT fault diagnosis system. The neural network training for the fault sample of TRT turbine rotor is carried out, and the fault diagnosis interface of TRT system is developed based on VB software. In this paper, the fault diagnosis technology is used to protect the unit and avoid the unnecessary stop caused by the instantaneous increase of the top pressure of the blast furnace, which provides a strong support for ensuring the long-term safety, stability, smooth running of the blast furnace TRT and improving the economic benefit.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 吳軍;火炮狀態(tài)智能診斷技術(shù)研究[D];南京理工大學(xué);2013年

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本文編號(hào)：1913206