發(fā)布時間：2018-09-18 13:48

【摘要】：過程監(jiān)測技術(shù)的出現(xiàn)是工業(yè)過程趨向自動化、智能化的標(biāo)志,作為保障系統(tǒng)安全穩(wěn)定運行的關(guān)鍵要素,其不可或缺性日益凸顯。以往樸素的過程監(jiān)測技術(shù)著眼于工業(yè)過程機(jī)理模型。然而,針對復(fù)雜程度日益提高的現(xiàn)代工業(yè)過程系統(tǒng),即便輔以先進(jìn)的模型辨識手段,基于物理化學(xué)先驗知識的精確過程模型也越來越難以構(gòu)建,這已成為工業(yè)控制學(xué)術(shù)界的共識。為此學(xué)術(shù)界將目光轉(zhuǎn)向由傳感器技術(shù)、網(wǎng)絡(luò)通信技術(shù)、計算機(jī)數(shù)據(jù)處理技術(shù)的發(fā)展而帶來的海量工業(yè)過程數(shù)據(jù)。顯然,這些工業(yè)過程歷史運行數(shù)據(jù)中蘊含著變量間的相關(guān)關(guān)系,充分挖掘數(shù)據(jù)內(nèi)部的信息將極大地助力過程監(jiān)測方法的研究�，F(xiàn)有的基于數(shù)據(jù)的過程監(jiān)測機(jī)制研究大多面向線性的、靜態(tài)的工業(yè)過程,雖然這些研究取得了一定的成果,但仍然難以滿足實際工業(yè)系統(tǒng)中非線性的過程監(jiān)測需求。針對這樣的情況,本文以偏最小二乘法為理論根基,試圖建立一套完整的工業(yè)過程監(jiān)測體系,使其能夠適應(yīng)于線性、非線性以及動態(tài)過程的監(jiān)測。本文首先介紹標(biāo)準(zhǔn)偏最小二乘算法原理,針對偏最小二乘算法的缺點,介紹一種以完全分解數(shù)據(jù)空間為核心思想的改進(jìn)型偏最小二乘算法,并討論該方法在故障檢測中的應(yīng)用,為后續(xù)算法的提出奠定理論基礎(chǔ)。接下來本文探討核偏最小二乘算法在非線性過程監(jiān)測中的應(yīng)用問題。在此基礎(chǔ)上,提出一種基于核偏最小二乘算法的在線非線性過程故障檢測方法。同時引入小波變換對數(shù)據(jù)情況復(fù)雜的非線性工業(yè)過程進(jìn)行監(jiān)測。以數(shù)值算例和污水處理系統(tǒng)為應(yīng)用背景,驗證所述方法的可行性,為監(jiān)測非線性、數(shù)據(jù)情況復(fù)雜的工業(yè)過程提供一種行之有效的解決方案。為了尋求動態(tài)過程監(jiān)測問題的解決方法,本文基于解構(gòu)過程數(shù)據(jù)以及過程模型的思想,將多子階段模型方法與核偏最小二乘法相結(jié)合,其核心思想是在對動態(tài)過程進(jìn)行監(jiān)測之前,先對考察的過程進(jìn)行精確建模。這一部分內(nèi)容給出了非線性動態(tài)工業(yè)過程監(jiān)測方法的新實踐。
[Abstract]:The appearance of process monitoring technology is the symbol of the trend of industrial process towards automation and intelligence. As a key element to ensure the safe and stable operation of the system, its indispensable is becoming more and more prominent. Previous simple process monitoring technology focused on the industrial process mechanism model. However, for modern industrial process systems with increasing complexity, even with advanced model identification methods, precise process models based on prior knowledge of physical chemistry are becoming more and more difficult to construct. This has become the consensus of industry control academia. For this reason, the academic circles turn their attention to the massive industrial process data brought by the development of sensor technology, network communication technology and computer data processing technology. Obviously, these industrial process historical running data contain the correlation relation between variables, fully mining the information inside the data will greatly help the research of the method of process monitoring. Most of the existing data-based process monitoring mechanisms are oriented to linear and static industrial processes. Although some achievements have been made in these studies, it is still difficult to meet the needs of nonlinear process monitoring in practical industrial systems. In this paper, based on the partial least square method, we try to establish a complete industrial process monitoring system, which can adapt to linear, nonlinear and dynamic process monitoring. In this paper, the principle of standard partial least squares algorithm is introduced, and an improved partial least squares algorithm based on the idea of completely decomposing data space is introduced, and its application in fault detection is discussed. It lays a theoretical foundation for the following algorithm. Then this paper discusses the application of kernel partial least squares algorithm in nonlinear process monitoring. On this basis, an on-line nonlinear process fault detection method based on kernel partial least squares algorithm is proposed. At the same time, wavelet transform is introduced to monitor nonlinear industrial processes with complex data. Taking numerical examples and sewage treatment system as the application background, the feasibility of the method is verified and an effective solution is provided for the monitoring of nonlinear and complex industrial processes. In order to find a solution to the problem of dynamic process monitoring, based on the idea of deconstruction process data and process model, this paper combines the multi-sub-stage model method with the kernel partial least square method. Its core idea is to model the process accurately before monitoring the dynamic process. In this part, the new practice of nonlinear dynamic industrial process monitoring method is given.
【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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本文編號：2248112