Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables

被引:7
|
作者
Xu, Yuxue [1 ]
Wang, Yun [2 ]
Yan, Tianhong [1 ]
He, Yuchen [1 ]
Wang, Jun [1 ]
Gu, De [3 ]
Du, Haiping [4 ]
Li, Weihua [5 ]
机构
[1] China Jiliang Univ, Coll Mech & Elect Engn, Hangzhou 310018, Peoples R China
[2] Zhejiang Tongji Vocat Coll Sci & Technol, Mech & Elect Engn Dept, Hangzhou 311231, Peoples R China
[3] Jiangnan Univ, Inst Automat, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Jiangsu, Peoples R China
[4] Univ Wollongong, Sch Elect Comp & Telecommun Engn, Wollongong, NSW 2522, Australia
[5] Univ Wollongong, Sch Mech Mat Mechatron & Biomed Engn, Wollongong, NSW 2522, Australia
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Soft sensor; Supervised Bayesian network; Latent variables; Locally weighted modeling; Quality prediction; TP277; PRINCIPAL COMPONENT REGRESSION; NAIVE BAYES; SENSOR; PREDICTION; ANALYTICS; MODEL;
D O I
10.1631/FITEE.2000426
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 quality-related 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.
引用
收藏
页码:1234 / 1246
页数:13
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