Full Text:   <1959>

Summary:  <1104>

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: 3030

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

-   Go to

Article info.
Open peer comments

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.

@article{title="Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables",
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"
}

%0 Journal Article
%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

TY - JOUR
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


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

Reference

[1]Al-Jlibawi A, Othman MLB, Al-Huseiny MS, et al., 2019. Efficient soft sensor modelling for advanced manufacturing systems by applying hybrid intelligent soft computing techniques. Int J Simul Syst Sci Technol, 19(3):15.1-15.7.

[2]Atkeson CG, Moore AW, Schaal S, 1997. Locally weighted learning. In: Aha DW (Ed.), Lazy Learning. Springer, Dordrecht, p.11-73.

[3]Ben‐Gal I, 2008. Bayesian networks. In: Ruggeri F, Kenett RS, Faltin FW (Eds.), Encyclopedia of Statistics in Quality and Reliability. John Wiley & Sons, Chichester, p.1.

[4]Bidar B, Sadeghi J, Shahraki F, et al., 2017. Data-driven soft sensor approach for online quality prediction using state dependent parameter models. Chemom Intell Lab Syst, 162:130-141.

[5]Bishop CM, 1998. Latent variable models. In: Jordan MI (Ed.), Learning in Graphical Models. MIT Press, Cambridge, p.371-403.

[6]Cain C, 2016. Modelling latent variables for Bayesian networks. Undergraduate Research Symp. Available from https://digitalcommons.morris.umn.edu/urs_2016/2/

[7]Chang SY, Baughman EH, McIntosh BC, 2001. Implementation of locally weighted regression to maintain calibrations on FT-NIR analyzers for industrial processes. Appl Spectrosc, 55(9):1199-1206.

[8]Chen GF, Yu HL, 2007. Bayesian network and its application in maize diseases diagnosis. Proc Int Conf on Computer and Computing Technologies in Agriculture, p.917-924.

[9]Fortuna L, Graziani S, Rizzo A, et al., 2007. Soft Sensors for Monitoring and Control of Industrial Processes. Springer, London, UK.

[10]Frank E, Trigg L, Holmes G, et al., 2000. Technical note: naive Bayes for regression. Mach Learn, 41(1):5-25.

[11]Frank E, Hall M, Pfahringer B, 2002. Locally weighted naive Bayes. Proc 19th Conf on Uncertainty in Artificial Intelligence, p.249-256.

[12]Ge ZQ, 2016. Supervised latent factor analysis for process data regression modeling and soft sensor application. IEEE Trans Contr Syst Technol, 24(3):1004-1011.

[13]Ge ZQ, 2018. Process data analytics via probabilistic latent variable models: a tutorial review. Ind Eng Chem Res, 57(38):12646-12661.

[14]Ge ZQ, Song ZH, Ding SX, et al., 2017. Data mining and analytics in the process industry: the role of machine learning. IEEE Access, 5:20590-20616.

[15]Geiger D, Heckerman D, 1994. Learning Gaussian networks. In: de Mantaras RL, Poole D (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Tenth Conference (1994). Morgan Kaufmann Publishers, California, USA, p.235-243.

[16]Jiang LX, Cai ZH, Zhang H, et al., 2013. Naive Bayes text classifiers: a locally weighted learning approach. J Exp Theor Artif Intell, 25(2):273-286.

[17]Kadlec P, Gabrys B, Strandt S, 2009. Data-driven soft sensors in the process industry. Comput Chem Eng, 33(4):795-814.

[18]Kadlec P, Grbić R, Gabrys B, 2011. Review of adaptation mechanisms for data-driven soft sensors. Comput Chem Eng, 35(1):1-24.

[19]Kano M, Fujiwara K, 2012. Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications. J Chem Eng Jpn, 46(1):1-17.

[20]Kim JS, Jun CH, 2013. Ranking evaluation of institutions based on a Bayesian network having a latent variable. Knowl-Based Syst, 50:87-99.

[21]Kim S, Okajima R, Kano M, et al., 2013. Development of soft-sensor using locally weighted PLS with adaptive similarity measure. Chemom Intell Lab Syst, 124:43-49.

[22]Li CQ, Jiang LX, Li HW, 2014. Naive Bayes for value difference metric. Front Comput Sci, 8(2):255-264.

[23]Liu F, Xu DX, Yuan C, et al., 2006. Image segmentation based on Bayesian network-Markov random field model and its application to in vivo plaque composition. Proc 3rd IEEE Int Symp on Biomedical Imaging: Nano to Macro, p.141-144.

[24]Liu Q, Zhuo J, Lang ZQ, et al., 2018. Perspectives on data-driven operation monitoring and self-optimization of industrial processes. Acta Autom Sin, 44(11):1944-1956 (in Chinese).

[25]Liu ZW, Ge ZQ, Chen GJ, et al., 2018. Adaptive soft sensors for quality prediction under the framework of Bayesian network. Contr Eng Pract, 72:19-28.

[26]Masmoudi K, Abid L, Masmoudi A, 2019. Credit risk modeling using Bayesian network with a latent variable. Expert Syst Appl, 127:157-166.

[27]Mohammadi A, Zarghami R, Lefebvre D, et al., 2019. Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis. J Adv Manuf Process, 1(4):e10027.

[28]Montáns FJ, Chinesta F, Gómez-Bombarelli R, et al., 2019. Data-driven modeling and learning in science and engineering. Compt Rend Mécan, 347(11):845-855.

[29]Murphy KP, 2001. The Bayes net toolbox for Matlab. Comput Sci Stat, 33(2):1024-1034.

[30]Nie SQ, Zheng M, Ji Q, 2018. The deep regression Bayesian network and its applications: probabilistic deep learning for computer vision. IEEE Signal Process Mag, 35(1):101-111.

[31]Shao WM, Tian XM, Wang P, 2015. Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor. Chin J Chem Eng, 23(12):1925-1934.

[32]Steurtewagen B, van den Poel D, 2020. Machine learning refinery sensor data to predict catalyst saturation levels. Comput Chem Eng, 134:106722.

[33]Tang H, Liu S, 2007. Basic theory of fuzzy Bayesian networks and its application in machinery fault diagnosis. Proc 4th Int Conf on Fuzzy Systems and Knowledge Discovery, p.132-137.

[34]Vallejo M, de la Espriella C, Gómez-Santamaría J, et al., 2019. Soft metrology based on machine learning: a review. Meas Sci Technol, 31(3):032001.

[35]Wang JB, Shao WM, Song ZH, 2019. Semi-supervised variational Bayesian student’s t mixture regression and robust inferential sensor application. Contr Eng Pract, 92: 104155.

[36]Wu J, Wu B, Pan SR, et al., 2015. Locally weighted learning: how and when does it work in Bayesian networks? Int J Comput Intell Syst, 8(S1):63-74.

[37]Yao L, Ge ZQ, 2017a. Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data. IEEE Trans Autom Sci Eng, 14(1):126-138.

[38]Yao L, Ge ZQ, 2017b. Moving window adaptive soft sensor for state shifting process based on weighted supervised latent factor analysis. Contr Eng Pract, 61:72-80.

[39]Yuan XF, Ge ZQ, Song ZH, 2014. Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes. Ind Eng Chem Res, 53(35):13736-13749.

[40]Yuan XF, Huang B, Ge ZQ, et al., 2016. Double locally weighted principal component regression for soft sensor with sample selection under supervised latent structure. Chemom Intell Lab Syst, 153:116-125.

[41]Yuan XF, Huang B, Wang YL, et al., 2018. Deep learning-based feature representation and its application for soft sensor modeling with variable-wise weighted SAE. IEEE Trans Ind Inform, 14(7):3235-3243.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE