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.
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.
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Yuxue XU and Yuchen HE designed the research. Yuchen HE and Yun WANG processed the data. Yuchen HE, Tianhong YAN, Haiping DU, and Weihua LI drafted the manuscript. Jun WANG helped polish the language. De GU helped organize the manuscript. Yuxue XU and Yuchen HE revised and finalized the paper.
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Yuxue XU, Yun WANG, Tianhong YAN, Yuchen HE, Jun WANG, De GU, Haiping DU, and Weihua LI declare that they have no conflict of interest.
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Project supported by the National Key Research and Development Program of China (No. 2016YFC0301404), the National Natural Science Foundation of China (Nos. 51379198 and 61903352), the Natural Science Foundation of Zhejiang Province, China (No. LQ19F030007), the Natural Science Foundation of Jiangsu Province, China (No. BK20180594), the Project of Department of Education of Zhejiang Province, China (No. Y202044960), the China Postdoctoral Science Foundation (No. 2020M671721), the Foundation of Key Laboratory of Advanced Process Control for Light Industry (No. APCLI1803), and the Fundamental Research Funds for the Provincial Universities of Zhejiang, China (Nos. 2021YW18 and 2021YW80)
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Xu, Y., Wang, Y., Yan, T. et al. Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network with latent variables. Front Inform Technol Electron Eng 22, 1234–1246 (2021). https://doi.org/10.1631/FITEE.2000426
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DOI: https://doi.org/10.1631/FITEE.2000426