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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

Clicked: 5101

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yuxue Xu

https://orcid.org/0000-0003-0144-6015

Yun Wang

https://orcid.org/0000-0002-7512-0168

Tianhong Yan

https://orcid.org/0000-0003-3916-3926

Yuchen He

https://orcid.org/0000-0002-0528-2778

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

http://doi.org/10.1631/FITEE.2000426


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",
volume="22",
number="9",
pages="1234-1246",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000426"
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%A Tianhong Yan
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A1 - Jun Wang
A1 - De Gu
A1 - Haiping Du
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Abstract: 
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.

基于含隐变量的贝叶斯网络质量相关局部加权的非平稳过程软测量方法

徐玉雪1,王云2,严天宏1,何雨辰1,王君1,顾德3,杜海平4,李卫华5
1中国计量大学机电工程学院,中国杭州市,310018
2浙江同济科技职业学院机电工程系,中国杭州市,311231
3江南大学自动化研究所轻工过程先进控制教育部重点实验室,中国无锡市,214122
4伍伦贡大学电气、计算机和电信工程学院,澳大利亚伍伦贡市,NSW2522
5伍伦贡大学机械、材料、机电和生物医学工程学院,澳大利亚伍伦贡市,NSW2522
摘要:在工业过程中,软测量技术被广泛用于预测难以测量的质量变量。构建一个应对过程非平稳性的自适应模型非常必要。本文针对非平稳过程,设计了一种基于含有隐变量贝叶斯网络的质量相关局部加权软测量方法。提出一种有监督贝叶斯网络提取质量相关的隐变量,并应用于一种双层相似度测量算法。所提软测量方法试图通过质量相关信息为非平稳过程寻找到一般方法,且详细解释了局部相似度和窗口置信度的概念。通过一个数值算例和脱丁烷塔的应用验证了所提方法的性能。结果表明所提方法预测关键质量变量的精确度优于竞争方法。

关键词:软测量;有监督贝叶斯网络;隐变量;局部加权建模;质量预测

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

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