本文選題：故障辨識　切入點：核偏最小二乘　出處：《中國安全生產(chǎn)科學(xué)技術(shù)》2015年12期

【摘要】：為適應(yīng)快速變化的化工產(chǎn)品需求,過程工業(yè)逐步向柔性生產(chǎn)發(fā)展,使得間歇過程的應(yīng)用日益廣泛。這一類工藝過程具有動態(tài)和非線性的特征,過程故障帶來的工藝波動和安全風(fēng)險是較為突出的挑戰(zhàn)。采用基于核函數(shù)的偏最小二乘方法,在高維特征空間提取特征變量,這些變量包含了生產(chǎn)過程的非線性結(jié)構(gòu)特征,也反應(yīng)了過程工況的模式特征。針對傳統(tǒng)線性方法存在的故障漏報等問題,利用核函數(shù)技巧,在特征空間進行數(shù)據(jù)重構(gòu),進而計算統(tǒng)計監(jiān)控指標(biāo)SPE,并通過對SPE的在線監(jiān)測實現(xiàn)更加有效地故障辨識。本方法針對標(biāo)準(zhǔn)非線性測試對象進行了過程監(jiān)測,實現(xiàn)結(jié)果充分說明了方法的有效性。
[Abstract]:In order to adapt to the rapidly changing demand of chemical products, the process industry is gradually developing to flexible production, which makes the batch process more and more widely used. This kind of process has the characteristics of dynamic and nonlinear. The process fluctuation and safety risk caused by process failure are more prominent challenges. Using the kernel function based partial least squares method, the feature variables are extracted in the high dimensional feature space, which contain the nonlinear structural features of the production process. It also reflects the mode characteristics of the process conditions. Aiming at the problems of the traditional linear method, such as missing the fault report, the kernel function technique is used to reconstruct the data in the feature space. Then the statistical monitoring index SPE is calculated, and the fault identification is realized more effectively by on-line monitoring of SPE. The process monitoring of standard nonlinear test object is carried out in this method, and the results fully demonstrate the effectiveness of the method.
【作者單位】： 中國安全生產(chǎn)科學(xué)研究院;重大危險源監(jiān)控與事故應(yīng)急技術(shù)國家安全監(jiān)管總局安全生產(chǎn)重點實驗室;
【基金】：國家科技支撐計劃項目(2015BAK16B04)
【分類號】：TQ086

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