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CLC number: TP277

On-line Access: 2021-09-10

Received: 2020-08-24

Revision Accepted: 2020-12-23

Crosschecked: 2021-07-22

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Yuxue Xu


Yun Wang


Tianhong Yan


Yuchen He


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.9 P.1234-1246


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

Author(s):  Yuxue Xu, Yun Wang, Tianhong Yan, Yuchen He, Jun Wang, De Gu, Haiping Du, Weihua Li

Affiliation(s):  College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China; more

Corresponding email(s):   thyan@cjlu.edu.cn, yche@cjlu.edu.cn

Key Words:  Soft sensor, Supervised Bayesian network, Latent variables, Locally weighted modeling, Quality prediction

Yuxue Xu, Yun Wang, Tianhong Yan, Yuchen He, Jun Wang, De Gu, Haiping Du, Weihua Li. Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(9): 1234-1246.

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author="Yuxue Xu, Yun Wang, Tianhong Yan, Yuchen He, Jun Wang, De Gu, Haiping Du, Weihua Li",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables
%A Yuxue Xu
%A Yun Wang
%A Tianhong Yan
%A Yuchen He
%A Jun Wang
%A De Gu
%A Haiping Du
%A Weihua Li
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 9
%P 1234-1246
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000426

T1 - Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables
A1 - Yuxue Xu
A1 - Yun Wang
A1 - Tianhong Yan
A1 - Yuchen He
A1 - Jun Wang
A1 - De Gu
A1 - Haiping Du
A1 - Weihua Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 9
SP - 1234
EP - 1246
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000426

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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