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基于共同趨勢模型的非平穩(wěn)過程在線監(jiān)控

發(fā)布時間:2018-04-15 16:44

  本文選題:共同趨勢模型 + 過程監(jiān)控; 參考:《化工學(xué)報》2017年01期


【摘要】:對于非平穩(wěn)過程監(jiān)控,傳統(tǒng)的基于數(shù)據(jù)平穩(wěn)假設(shè)的多元統(tǒng)計過程控制方法是不適用的。針對上述問題,提出了一種基于共同趨勢模型的非平穩(wěn)過程監(jiān)控方法。共同趨勢模型從存在協(xié)整關(guān)系的非平穩(wěn)多元變量中辨識出共同因子,將各非平穩(wěn)過程變量分解成非平穩(wěn)的共同趨勢成分與平穩(wěn)成分之和的形式。不同于現(xiàn)有的基于協(xié)整模型的非平穩(wěn)過程監(jiān)控方法,共同趨勢模型能夠獲取各非平穩(wěn)變量中的平穩(wěn)成分,消除非平穩(wěn)共同因子的影響并體現(xiàn)變量間全部的動態(tài)均衡關(guān)系。將對非平穩(wěn)過程的監(jiān)控變?yōu)閼?yīng)用共同趨勢模型,分解得到各非平穩(wěn)過程變量中的平穩(wěn)成分,然后應(yīng)用傳統(tǒng)的多元統(tǒng)計方法,估計平穩(wěn)成分的統(tǒng)計量及相應(yīng)的控制限進(jìn)行監(jiān)測。石油蒸餾過程監(jiān)控的實(shí)例研究結(jié)果表明,所提出的方法比基于協(xié)整新息變量的方法具有更可靠的監(jiān)控效果。
[Abstract]:For non-stationary process monitoring, the traditional multivariate statistical process control method based on stationary assumption is not applicable.In order to solve the above problems, a method of monitoring non-stationary processes based on common trend model is proposed.The common trend model identifies common factors from non-stationary multivariate variables with cointegration relationship and decomposes each non-stationary process variable into the sum of non-stationary common trend components and stationary components.Different from the existing non-stationary process monitoring methods based on cointegration model, the common trend model can obtain the stationary components of the non-stationary variables, eliminate the influence of the non-stationary common factors and reflect the dynamic equilibrium relationship among the variables.The monitoring of non-stationary processes is transformed into a common trend model, and the stationary components in the variables of non-stationary processes are decomposed. Then, the statistics of stationary components and the corresponding control limits are estimated and monitored by using the traditional multivariate statistical method.The experimental results of oil distillation process monitoring show that the proposed method is more reliable than the one based on cointegration innovation variables.
【作者單位】: 南京航空航天大學(xué)機(jī)械結(jié)構(gòu)力學(xué)及控制國家重點(diǎn)實(shí)驗(yàn)室;
【基金】:江蘇高校優(yōu)勢學(xué)科建設(shè)工程資助項(xiàng)目~~
【分類號】:TP277

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