本文選題：關(guān)鍵設(shè)備 + 特征提取　； 參考：《濟南大學(xué)》2015年碩士論文

【摘要】：水泥生產(chǎn)中的每個環(huán)節(jié)都是相互聯(lián)系的,任何環(huán)節(jié)出現(xiàn)問題都會降低水泥生產(chǎn)的效率和質(zhì)量。而水泥生產(chǎn)線中關(guān)鍵設(shè)備的正常工作是保證各生產(chǎn)環(huán)節(jié)正常運行的重要前提。水泥生產(chǎn)線長、生產(chǎn)設(shè)備結(jié)構(gòu)復(fù)雜且數(shù)量眾多、加上水泥生產(chǎn)環(huán)境惡劣,一旦某一生產(chǎn)環(huán)節(jié)的關(guān)鍵設(shè)備出現(xiàn)故障,都會發(fā)生一系列的連鎖反應(yīng),造成嚴重的生產(chǎn)問題。從長遠發(fā)展角度來看,故障診斷技術(shù)必然是未來科學(xué)研究的熱點內(nèi)容。同理,將故障診斷技術(shù)運用到水泥生產(chǎn)設(shè)備當中也必定是水泥行業(yè)發(fā)展的一個重要方向。針對以上分析,展開對水泥生產(chǎn)關(guān)鍵設(shè)備故障診斷系統(tǒng)的研究具有十分重要的實用價值和實際意義。本文以某水泥生產(chǎn)現(xiàn)場作為課題的研究背景,對水泥生產(chǎn)關(guān)鍵設(shè)備的故障診斷系統(tǒng)進行研究。課題研究旨在研究思想可以為整條水泥生產(chǎn)中關(guān)鍵設(shè)備的故障診斷方法提供具有實際意義的參考依據(jù),開發(fā)的故障診斷系統(tǒng)在生產(chǎn)現(xiàn)場能夠?qū)崿F(xiàn)預(yù)定的故障診斷功能。在深入了解水泥生產(chǎn)工藝的基礎(chǔ)上,得出水泥生產(chǎn)過程中的關(guān)鍵設(shè)備,選取窯尾高溫風(fēng)機為課題的研究對象。課題研究首先提取出能表征高溫風(fēng)機現(xiàn)場運行狀態(tài)的關(guān)鍵特征值參數(shù),根據(jù)生產(chǎn)現(xiàn)場參數(shù)的設(shè)定值,判斷特征參數(shù)是否處于正常變化范圍。其次,仿真從現(xiàn)場采集到的高溫風(fēng)機不同運行狀態(tài)下的關(guān)鍵參數(shù)數(shù)據(jù),得出不同故障類型下各特征參數(shù)的不同變化趨勢。把模糊推理和基于案例推理算法用于水泥生產(chǎn)關(guān)鍵設(shè)備故障診斷算法研究。在對設(shè)備不同故障類型下關(guān)鍵參數(shù)變化趨勢分析的基礎(chǔ)上,通過模糊推理得出故障診斷規(guī)則;把得到的故障診斷規(guī)則存入規(guī)則庫中,為案例推理提供參考。在案例推理算法中,運用聚類方法對不同運行狀態(tài)的數(shù)據(jù)進行歸類,得出表征設(shè)備不同運行狀態(tài)的代表性數(shù)據(jù);通過相似度的比較判斷案例的匹配程度,得出設(shè)備最可能的故障狀態(tài)。最后,對水泥生產(chǎn)關(guān)鍵設(shè)備故障診斷系統(tǒng)軟件進行設(shè)計與開發(fā)。從工業(yè)生產(chǎn)的實際應(yīng)用出發(fā),在基于Asea Brown Boveri Ltd(ABB)公司的Freelance AC800F系統(tǒng)平臺的基礎(chǔ)上對故障診斷系統(tǒng)進行開發(fā),運用Microsoft Visual Basic 6.0編寫水泥生產(chǎn)關(guān)鍵設(shè)備故障診斷系統(tǒng)軟件。故障診斷系統(tǒng)軟件的架構(gòu)由OLE for Process Control(OPC)數(shù)據(jù)采集系統(tǒng)、故障診斷前臺界面以及后臺SQL Server 2000數(shù)據(jù)庫三部分組成。運用開發(fā)的故障診斷系統(tǒng)對設(shè)備不同運行狀態(tài)歷史數(shù)據(jù)進行仿真實驗得到了很好的仿真效果。
[Abstract]:Every link in cement production is related to each other. Any problems in cement production will reduce the efficiency and quality of cement production. The normal operation of key equipment in cement production line is an important prerequisite to ensure the normal operation of each production link. The cement production line is long, the production equipment structure is complex and the quantity is numerous, plus the cement production environment is bad, once the key equipment of a certain production link breaks down, will have a series of chain reaction, cause the serious production problem. From the view of long-term development, fault diagnosis technology is bound to be a hot topic of scientific research in the future. Similarly, the application of fault diagnosis technology to cement production equipment must be an important direction of cement industry development. In view of the above analysis, it is of great practical value and practical significance to study the fault diagnosis system of key equipment in cement production. In this paper, a cement production site as the research background, the cement production key equipment fault diagnosis system is studied. The purpose of the research is to provide a practical reference for the fault diagnosis method of the key equipment in the whole cement production. The developed fault diagnosis system can realize the predetermined fault diagnosis function in the production site. On the basis of deep understanding of cement production technology, the key equipment in cement production process is obtained, and the high temperature fan at kiln tail is selected as the research object. The key eigenvalue parameters which can represent the running state of the high temperature fan are extracted firstly. According to the set values of the production field parameters, the characteristic parameters are judged to be in the normal range. Secondly, the key parameter data of high temperature fan in different operation state are simulated, and the variation trend of each characteristic parameter under different fault type is obtained. Fuzzy reasoning and case-based reasoning are applied to fault diagnosis algorithm of critical equipment in cement production. Based on the analysis of the variation trend of key parameters under different fault types of equipment, the fault diagnosis rules are obtained by fuzzy reasoning, and the obtained fault diagnosis rules are stored in the rule base, which provides a reference for CBR. In the Case-Based reasoning algorithm, clustering method is used to classify the data of different running states, and the representative data representing the different running states of the equipment are obtained, and the matching degree of the case is judged by the comparison of similarity. Get the most likely failure state of the equipment. Finally, the software of fault diagnosis system for key equipment in cement production is designed and developed. Starting from the practical application of industrial production, the fault diagnosis system is developed on the basis of Freelance AC800F system platform of Asea Brown Boveri Ltd. ABB, and the software of fault diagnosis system for key equipment of cement production is programmed by Microsoft Visual Basic 6.0. The software architecture of fault diagnosis system is composed of OLE for Process Control OPC data acquisition system, front interface of fault diagnosis and background SQL Server 2000 database. By using the developed fault diagnosis system, the simulation results of different operation state history data of the equipment are obtained.
【學(xué)位授予單位】：濟南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TQ172.6

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相關(guān)期刊論文 前1條

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本文編號：1839618