【摘要】：隨著半導(dǎo)體技術(shù)、制造工藝、通訊技術(shù)和網(wǎng)絡(luò)技術(shù)的飛速發(fā)展,現(xiàn)代工業(yè)生產(chǎn)自動化水平日益提高,控制系統(tǒng)復(fù)雜程度隨之加大。生產(chǎn)過程中的各儀表測量值是否能準(zhǔn)確反應(yīng)生產(chǎn)過程的狀態(tài),對于工業(yè)生產(chǎn)過程的安全性,控制系統(tǒng)的可靠性,保證工業(yè)產(chǎn)品質(zhì)量有著至關(guān)重要的作用。工業(yè)儀表作為控制系統(tǒng)的關(guān)鍵部件之一,由于其材料工藝,制造生產(chǎn)技術(shù)以及工作環(huán)境等因素,工業(yè)儀表在整個控制系統(tǒng)中較容易發(fā)生故障�？焖�、準(zhǔn)確檢測出儀表故障并采取相關(guān)的正確策略,是保證控制系統(tǒng)穩(wěn)定運(yùn)行、消除生產(chǎn)安全隱患的關(guān)鍵,具有十分重要的意義。 本文針對生產(chǎn)工業(yè)過程中多儀表的具體情況,采用基于數(shù)據(jù)驅(qū)動的故障檢測方法,實現(xiàn)了對生產(chǎn)工業(yè)過程中多儀表的故障檢測,并將所研究方法應(yīng)用于Tennessee Eastman仿真平臺,模擬不同的儀表故障信號,對檢測結(jié)果進(jìn)行對比分析。本文的主要工作如下： 1)針對工業(yè)生產(chǎn)數(shù)據(jù)并不符合高斯分布這一特殊的數(shù)據(jù)分布情況,并結(jié)合生產(chǎn)過程中多儀表的狀況,引入了基于獨立元分析的故障檢測方法。通過對歷史正常數(shù)據(jù)進(jìn)行分離提取源信息,建立故障檢測模型,并通過核密度估計的方法確定控制限,實現(xiàn)了多儀表故障檢測。通過與基于主元分析的故障檢測方法進(jìn)行比較分析,驗證了基于獨立元分析的故障檢測方法更適于工業(yè)生產(chǎn)過程中的儀表故障檢測。 2)針對工業(yè)生產(chǎn)數(shù)據(jù)中存在的高斯源信息和非高斯源信息,以主元分析法和獨立元分析法為主要理論依據(jù),通過對工業(yè)生產(chǎn)過程數(shù)據(jù)不同信息的提取和分離,分別采用相應(yīng)的分析方法,建立不同的故障檢測模型。仿真結(jié)果表明,相較于采用單一的基于獨立元分析的故障檢測方法,將獨立元分析法和主元分析方法結(jié)合使用具有更好的檢測效果。 3)在基于貢獻(xiàn)度的獨立元子空間理論方法基礎(chǔ)上,對子空間的故障檢測模型進(jìn)行完善。在儀表微小故障難以檢測的問題上加以應(yīng)用,根據(jù)不同的實際需求提供相應(yīng)的故障集成檢測策略。仿真結(jié)果基于貢獻(xiàn)度的獨立元子空間方法和完善的故障檢測模型提高了儀表微小故障的檢測效果,不同的集成策略更加具有靈活性和應(yīng)用性。 本文通過引入不同的故障檢測理論方法對工業(yè)生產(chǎn)過程多儀表情況下的故障進(jìn)行檢測,對檢測結(jié)果進(jìn)行分析探討。可以對工業(yè)過程多儀表的故障檢測提供新思路。
[Abstract]:With the rapid development of semiconductor technology, manufacturing technology, communication technology and network technology, the automation level of modern industrial production has been improved day by day, and the complexity of control system has increased. It is very important for the safety of the industrial production process, the reliability of the control system and the quality of the industrial products whether the measured values of each instrument in the production process can accurately reflect the state of the production process. As one of the key components of the control system, industrial instruments are prone to malfunction in the whole control system due to the factors such as material technology, manufacturing technology and working environment. It is of great significance to detect the instrument failure quickly and accurately and adopt the correct strategy to ensure the stable operation of the control system and eliminate the hidden trouble of production safety. In this paper, according to the specific situation of multi-instrument in the process of production industry, the fault detection method based on data drive is adopted to realize the fault detection of multi-instrument in the process of production industry, and the research method is applied to the simulation platform of Tennessee Eastman. The fault signals of different instruments are simulated and the test results are compared and analyzed. The main work of this paper is as follows: 1) aiming at the fact that the industrial production data do not accord with the special data distribution of Gao Si, and considering the situation of multiple instruments in the production process, a fault detection method based on independent element analysis is introduced. Through separating and extracting the source information from the historical normal data, the fault detection model is established, and the control limit is determined by the method of kernel density estimation, which realizes the fault detection of multiple instruments. Compared with the fault detection method based on principal component analysis, It is verified that the fault detection method based on independent element analysis is more suitable for instrument fault detection in industrial production process. 2) aiming at Gao Si source information and non-Gao Si source information in industrial production data, Based on principal component analysis (PCA) and independent component analysis (ICA), different fault detection models were established by extracting and separating different information from industrial process data. The simulation results show that, compared with the single fault detection method based on independent element analysis, The combination of independent component analysis and principal component analysis has better detection effect. 3) the fault detection model of subspace is improved on the basis of independent subspace theory and method based on contribution degree. It is applied to the problem that it is difficult to detect the small fault of the instrument, and the corresponding integrated detection strategy is provided according to the actual demand. The simulation results show that the independent subspace method based on the contribution degree and the perfect fault detection model can improve the detection effect of the instrument micro-fault, and the different integration strategies are more flexible and applicable. In this paper, different fault detection theories and methods are introduced to detect the faults under the condition of multiple instruments in industrial production process, and the detection results are analyzed and discussed. It can provide a new idea for the fault detection of multiple instruments in industrial process.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TH165.3

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本文編號：2268042