本文選題：過程監(jiān)測 + 實時學(xué)習(xí)��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：過程監(jiān)測在保證系統(tǒng)的安全性和可靠性方面起著至關(guān)重要的作用,對于現(xiàn)代工業(yè)過程的發(fā)展來說是一個關(guān)鍵的研究課題。隨著社會的發(fā)展,現(xiàn)代工業(yè)過程變得日益智能化,系統(tǒng)內(nèi)部結(jié)構(gòu)錯綜復(fù)雜,很難通過機理分析或定性分析達到建立數(shù)學(xué)模型的目的,因而在過程監(jiān)測方面,傳統(tǒng)的基于機理模型的方法和基于知識的方法具有非常大的局限性,在現(xiàn)代工業(yè)系統(tǒng)中,難以被廣泛的應(yīng)用和推廣。值得注意的是,現(xiàn)代工業(yè)過程在系統(tǒng)運行時往往會產(chǎn)生大量隱含過程特性信息的歷史數(shù)據(jù)和實時數(shù)據(jù),如何充分挖掘這些數(shù)據(jù)中的有效信息為過程監(jiān)測提供新的思路成為國內(nèi)外學(xué)者普遍關(guān)注的重點,這也是基于數(shù)據(jù)的過程監(jiān)測方法的研究內(nèi)容。目前,已有的過程監(jiān)測算法大多是針對線性過程而提出的,但實際上,現(xiàn)代工業(yè)過程常常是復(fù)雜的非線性動態(tài)系統(tǒng)。針對此現(xiàn)實,本文將在線性靜態(tài)系統(tǒng)過程監(jiān)測思路的基礎(chǔ)上,提出一種用于非線性動態(tài)系統(tǒng)的過程監(jiān)測方法,為解決上述難題提供一種新的研究方法和思路。首先,本文采用一種實時學(xué)習(xí)方法用于解決復(fù)雜非線性動態(tài)系統(tǒng)難以精確建模的問題,結(jié)合多變量統(tǒng)計過程監(jiān)測中最經(jīng)典的主元統(tǒng)計法,實現(xiàn)對現(xiàn)代工業(yè)系統(tǒng)的過程監(jiān)測,同時為后續(xù)算法的提出提供解決思路。然后,本文在基于實時學(xué)習(xí)的主元統(tǒng)計分析法實現(xiàn)思路上,針對現(xiàn)代工業(yè)過程常常受確定擾動的問題,提出一種適用于線性靜態(tài)系統(tǒng)的過程監(jiān)測算法,該算法通過殘差評估進行故障決策,并將其與實時學(xué)習(xí)建模算法相結(jié)合,用于復(fù)雜非線性動態(tài)系統(tǒng)的過程監(jiān)測。同時將提出的算法與其他非線性算法數(shù)值仿真對比,以說明提出的算法在故障誤檢率、漏檢率以及魯棒性等性能方面具有優(yōu)勢。最后,本文介紹污水處理過程采用基于數(shù)據(jù)方法的必要性,并將提出算法與其他非線性算法均應(yīng)用到污水處理過程中,實驗分析證明本文所提方法的先進性。
[Abstract]:Process monitoring plays an important role in ensuring the safety and reliability of the system and is a key research topic for the development of modern industrial processes. With the development of society, the modern industrial process becomes more and more intelligent, the internal structure of the system is complicated, it is difficult to establish the mathematical model through mechanism analysis or qualitative analysis, so in the aspect of process monitoring, The traditional methods based on mechanism model and knowledge have great limitations and are difficult to be widely applied and popularized in modern industrial systems. It is worth noting that modern industrial processes often produce a large number of historical and real-time data with implicit process characteristics information when the system is running. How to fully mine the effective information from these data to provide new ideas for process monitoring has become the focus of attention of scholars at home and abroad, which is also the research content of data-based process monitoring methods. At present, most of the existing process monitoring algorithms are proposed for linear processes, but in fact, modern industrial processes are often complex nonlinear dynamic systems. In view of this reality, based on the idea of process monitoring in linear static systems, a process monitoring method for nonlinear dynamic systems is proposed in this paper, which provides a new research method and train of thought for solving the above problems. First of all, a real-time learning method is used to solve the problem of complex nonlinear dynamic system which is difficult to model accurately, and the most classical principal component statistics method in multivariable statistical process monitoring is used to realize the process monitoring of modern industrial system. At the same time, it provides the solution for the following algorithm. Then, based on the realization of principle component statistical analysis method based on real-time learning, a process monitoring algorithm for linear static systems is proposed to solve the problem that modern industrial processes are often subject to deterministic disturbances. The algorithm is applied to process monitoring of complex nonlinear dynamic systems by residual evaluation, which is combined with real-time learning modeling algorithm. At the same time, the proposed algorithm is compared with other nonlinear algorithms to show that the proposed algorithm has advantages in fault error detection rate, miss detection rate and robustness. Finally, this paper introduces the necessity of adopting data-based method in the process of sewage treatment, and applies the proposed algorithm and other nonlinear algorithms to the process of sewage treatment. The experimental analysis proves the advanced nature of the proposed method.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP274

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