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

doi:10.1631/FITEE.2000426

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

Regular Papers | Updated：2022-05-19

- Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent 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
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 9, Pages: 1234-1246(2021)
- Affiliations：
  
  College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
  Mechanical and Electrical Engineering Department, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou 311231, China
  Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, China
  School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
  School of Mechanical, Materials, Mechatronic, and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
- Author bio：
  
  Tianhong YAN, E-mail: thyan@cjlu.edu.cn
  Yuchen HE, E-mail: yche@cjlu.edu.cn
- Funds：
- DOI：10.1631/FITEE.2000426
  CLC： TP277
- Received：24 August 2020，
  
  Revised：2021-;7-22，
  
  Published：2021-09
- Accepted：
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Yuxue XU, Yun WANG, Tianhong YAN, et al. 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.
DOI：

Yuxue XU, Yun WANG, Tianhong YAN, et al. 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. DOI： 10.1631/FITEE.2000426.

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。

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.

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