Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables
Yuxue XU , Yun WANG , Tianhong YAN , Yuchen HE , Jun WANG , De GU , Haiping DU , Weihua LI
Front. Inform. Technol. Electron. Eng ›› 2021, Vol. 22 ›› Issue (9) : 1234 -1246.
Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via qualityrelated information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.
Soft sensor / Supervised Bayesian network / Latent variables / Locally weighted modeling / Quality prediction
Zhejiang University Press
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