發(fā)布時(shí)間：2018-05-21 15:35

  本文選題：基于數(shù)據(jù)驅(qū)動(dòng)方法 + 主元分析��； 參考：《中南大學(xué)》2012年碩士論文

【摘要】：動(dòng)態(tài)系統(tǒng)的安全、穩(wěn)定和高效運(yùn)行一直是工業(yè)界十分關(guān)注的問(wèn)題,尤其是擔(dān)負(fù)有生產(chǎn)任務(wù)的動(dòng)態(tài)系統(tǒng),對(duì)其生產(chǎn)過(guò)程控制的品質(zhì)提出了更高的要求,產(chǎn)品的質(zhì)量,產(chǎn)品的故障率以及生產(chǎn)過(guò)程是否滿(mǎn)足日益嚴(yán)格的安全和環(huán)境要求等問(wèn)題時(shí)刻被關(guān)注。開(kāi)展對(duì)動(dòng)態(tài)系統(tǒng)故障診斷研究將是非常有必要而且能為企業(yè)運(yùn)行節(jié)約經(jīng)濟(jì)成本,同時(shí)也能為環(huán)境友好工程和可持續(xù)發(fā)展帶來(lái)新的方法和研究課題。 本文對(duì)主元分析故障診斷方法的基本原理進(jìn)行了介紹,針對(duì)傳統(tǒng)主元分析在監(jiān)測(cè)動(dòng)態(tài)系統(tǒng)具有多變量耦合、時(shí)變、大滯后等特性時(shí)容易出現(xiàn)誤報(bào)和漏報(bào),以及動(dòng)態(tài)主元分析在進(jìn)行在線(xiàn)監(jiān)測(cè)時(shí)反應(yīng)不夠迅速等的問(wèn)題,研究了基于遞歸動(dòng)態(tài)主元分析的故障檢測(cè)方法。同時(shí),在深入分析了舊統(tǒng)計(jì)量的基礎(chǔ)之上,研究了兩個(gè)綜合統(tǒng)計(jì)量,并給出了具體的表達(dá)式和閾值計(jì)算公式。通過(guò)實(shí)際密閉鼓風(fēng)爐數(shù)據(jù),建立了遞歸動(dòng)態(tài)主元分析模型,結(jié)合兩個(gè)新的統(tǒng)計(jì)檢測(cè)量與動(dòng)態(tài)主元分析進(jìn)行了比較,實(shí)驗(yàn)結(jié)果證明了遞歸動(dòng)態(tài)主元分析方法的準(zhǔn)確性和可行性,且新提出的監(jiān)測(cè)統(tǒng)計(jì)量也能很好的應(yīng)用在對(duì)爐況的在線(xiàn)監(jiān)測(cè)中。 在研究了子空間辨識(shí)方法的基礎(chǔ)上,本文對(duì)基于主元分析方法和子空間辨識(shí)方法集成的故障檢測(cè)算法(SIMPCA)進(jìn)行了研究,通過(guò)合理的構(gòu)造觀(guān)測(cè)數(shù)據(jù)矩陣,利用SIMPCA方法建立了過(guò)程的監(jiān)測(cè)模型,通過(guò)理論分析,該算法能消除隨機(jī)干擾和噪聲的影響,使得到的殘差僅和故障信號(hào)有關(guān)。隨后,通過(guò)CSTH模型仿真研究,驗(yàn)證了SIMPCA故障檢測(cè)算法進(jìn)行過(guò)程監(jiān)測(cè)的可行性和有效性。通過(guò)正常運(yùn)行過(guò)程和異常過(guò)程的監(jiān)測(cè)對(duì)比分析,SIMPCA故障檢測(cè)算法能有效區(qū)分過(guò)程的正常和異常運(yùn)行情況,準(zhǔn)確檢測(cè)出異常的發(fā)生。
[Abstract]:The safety, stability and efficient operation of a dynamic system have always been a matter of great concern in the industry, especially the dynamic system that bears the production task, which puts forward higher requirements for the quality of its production process control, the quality of the product, the failure rate of the product, and whether the production process meets the increasingly stringent safety and environmental requirements. The research on dynamic system fault diagnosis will be very necessary and can save the economic cost for the operation of the enterprise, and also bring new methods and research topics for environmental friendly engineering and sustainable development.
In this paper, the basic principle of the principal component analysis fault diagnosis method is introduced. In view of the traditional principal component analysis, it is easy to misreport and misreport when the dynamic system has the characteristics of multivariable coupling, time-varying, large lag and so on, and the dynamic principal component analysis is not fast enough in the on-line monitoring. At the same time, on the basis of the analysis of the old statistics, two comprehensive statistics are studied, and the concrete expressions and the formula of the threshold calculation are given. The recursive dynamic principal component analysis model is established through the actual closed blast furnace data, and two new statistical detection quantities and dynamic principal component analysis are combined. The experimental results prove the accuracy and feasibility of the recursive dynamic principal component analysis method, and the new monitoring statistics can be well applied to the on-line monitoring of the condition of the furnace.
On the basis of studying the subspace identification method, the fault detection algorithm (SIMPCA) based on the principal component analysis method and the subspace identification method is studied. Through the rational construction of the observation data matrix, the SIMPCA method is used to establish the monitoring model of the process. Through the theoretical analysis, the algorithm can eliminate the random interference and noise. The effect of sound is only related to the fault signal. Then, the feasibility and effectiveness of the process monitoring of the SIMPCA fault detection algorithm are verified by the CSTH model simulation research. The SIMPCA fault detection algorithm can effectively distinguish the normal and abnormal operation of the process through the comparison and analysis of the normal operation process and the abnormal process monitoring. Situation, accurate detection of abnormal occurrence.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TH165.3

【引證文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

1 蔣少華;基于數(shù)據(jù)驅(qū)動(dòng)的密閉鼓風(fēng)爐故障診斷及預(yù)測(cè)研究[D];中南大學(xué);2009年

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本文編號(hào)：1919783