本文選題：經(jīng)驗(yàn)?zāi)B(tài)分解 + 可見(jiàn)圖��； 參考：《北京化工大學(xué)》2015年碩士論文

【摘要】：現(xiàn)今化工過(guò)程生產(chǎn)工藝日趨復(fù)雜、規(guī)模日漸龐大,加之化工數(shù)據(jù)本身維度高、數(shù)量大、相關(guān)性強(qiáng)且充斥噪聲等特點(diǎn),無(wú)不令安全生產(chǎn)和產(chǎn)品質(zhì)量面臨更大挑戰(zhàn)和更高要求。為此,本文利用經(jīng)驗(yàn)?zāi)B(tài)分解(EMD)按照頻率尺度自適應(yīng)處理信號(hào)提取故障特征的能力,以及多變量經(jīng)驗(yàn)?zāi)B(tài)分解(MEMD)處理關(guān)聯(lián)信息的優(yōu)勢(shì),對(duì)其在化工過(guò)程中的故障診斷應(yīng)用探索研究,同時(shí)結(jié)合改進(jìn)的動(dòng)態(tài)可見(jiàn)圖算法,提出一種數(shù)據(jù)驅(qū)動(dòng)的故障診斷方法,應(yīng)用于田納西-伊士曼(TE)過(guò)程的實(shí)時(shí)監(jiān)測(cè)和在線診斷,仿真結(jié)果驗(yàn)證了提出方法的有效性與優(yōu)越性。主要研究?jī)?nèi)容涵蓋如下：首先,利用殘差對(duì)故障的敏感性,提出基于總體平均EMD(EEMD)殘差的故障診斷方法。根據(jù)歷史數(shù)據(jù)的66控制圖,確定殘差的故障診斷控制限；利用在線實(shí)時(shí)數(shù)據(jù)采用貝葉斯信息準(zhǔn)則在線確定EEMD的移動(dòng)窗口；通過(guò)移動(dòng)窗口的采樣數(shù)據(jù),在線獲得EEMD殘差最大值的變化,結(jié)合相應(yīng)的故障診斷控制限在線診斷故障并確定故障發(fā)生時(shí)間及原因。其次,為提高過(guò)程監(jiān)測(cè)的效率和精度,克服單一變量監(jiān)測(cè)的局限性,提出一種基于改進(jìn)動(dòng)態(tài)可見(jiàn)圖(MDVG)算法的多變量過(guò)程在線故障監(jiān)測(cè)方法。通過(guò)數(shù)據(jù)歸一化和引入時(shí)間間隔常數(shù),改進(jìn)動(dòng)態(tài)可見(jiàn)圖(DVG),使得DVG所關(guān)注特性的眾數(shù)出現(xiàn)次數(shù)的均值最小,以細(xì)分不同時(shí)序數(shù)據(jù)的網(wǎng)絡(luò)特性。最后,結(jié)合MEMD和MDVG,提出MEMD-MDVG故障診斷方法。利用MEMD殘差確定監(jiān)測(cè)變量,將各個(gè)監(jiān)測(cè)變量的歷史數(shù)據(jù)利用M MDVG確定監(jiān)測(cè)指標(biāo)及閾值并實(shí)施在線監(jiān)測(cè),異常情況下再借助MEMD殘差進(jìn)行相關(guān)性分析以確定故障原因。
[Abstract]:Nowadays the production process of chemical process is becoming more and more complex and the scale is increasing. In addition the chemical data itself has the characteristics of high dimension large quantity strong correlation and full of noise. All of them make the safety production and product quality face more challenges and higher requirements. Therefore, this paper makes use of the ability of EMD) to extract fault features according to frequency scale adaptive signal processing, and the advantage of multivariable empirical mode decomposition (MEMD) in processing associated information. A data-driven fault diagnosis method is proposed, which is applied to real-time monitoring and on-line diagnosis of Tennessee Eastman (TET) process. Simulation results verify the effectiveness and superiority of the proposed method. The main contents are as follows: firstly, using the sensitivity of residuals to faults, a fault diagnosis method based on the total average EMDEEMD residuals is proposed. According to the 66 control chart of historical data, the fault diagnosis control limit of residuals is determined; the moving window of EEMD is determined online by using online real-time data using Bayesian information criterion; the sampling data of moving window is sampled through the moving window. The variation of the maximum residual error of EEMD is obtained online, and the time and reason of fault occurrence are determined by combining with the corresponding fault diagnosis control limit. Secondly, in order to improve the efficiency and precision of process monitoring and overcome the limitation of single variable monitoring, a method of on-line fault monitoring for multivariable process based on improved dynamic visibility map (MDVG) algorithm is proposed. By means of data normalization and the introduction of time interval constant, the dynamic visibility map (DVG) is improved to minimize the average number of modes of appearance of the characteristics concerned by DVG, so as to subdivide the network characteristics of different time series data. Finally, combined with MEMD and MDVG, the method of MEMD-MDVG fault diagnosis is proposed. The monitoring variables are determined by MEMD residuals, and the monitoring indexes and thresholds are determined by M MDVG with the historical data of each monitoring variable, and on-line monitoring is carried out. In abnormal cases, the correlation analysis of MEMD residuals is used to determine the causes of faults.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TQ02

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 焦彥軍;胡春;;基于改進(jìn)EEMD方法的數(shù)字濾波器[J];電力自動(dòng)化設(shè)備;2011年11期

2 胡維平;莫家玲;杜明輝;;經(jīng)驗(yàn)?zāi)B(tài)分解中的頻域分辨率及其改進(jìn)方法[J];華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2007年05期

3 孫偉峰;彭玉華;楊陽(yáng);孟慶芳;許建華;;經(jīng)驗(yàn)?zāi)B(tài)分解頻率分辨率的一種改進(jìn)方法[J];計(jì)算機(jī)工程與應(yīng)用;2010年01期

4 程軍圣;于德介;楊宇;;Hilbert-Huang變換端點(diǎn)效應(yīng)問(wèn)題的探討[J];振動(dòng)與沖擊;2005年06期

5 鞠萍華;秦樹(shù)人;秦毅;丁志宇;;多分辨EMD方法與頻域平均在齒輪早期故障診斷中的研究[J];振動(dòng)與沖擊;2009年05期

6 時(shí)培明;丁雪娟;李庚;韓東穎;;一種EMD改進(jìn)方法及其在旋轉(zhuǎn)機(jī)械故障診斷中的應(yīng)用[J];振動(dòng)與沖擊;2013年04期

，

本文編號(hào)：1792736