首页|A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

A Soft Sensor with Light and Efficient Multi-scale Feature Method for Multiple Sampling Rates in Industrial Processing

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In industrial process control systems,there is overwhelming evidence corroborating the notion that economic or technical limitations result in some key variables that are very difficult to measure online.The data-driven soft sensor is an effective solution be-cause it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale fea-ture extraction layers to build soft sensors,which are applied to the benchmarked Tennessee-Eastman process(TEP)and a real wind farm case.The comparison of modelling results demonstrates that the multiscale feature extraction layers have the following advant-ages over other methods.First,the multiscale feature extraction layers significantly reduce the number of parameters compared to the other deep neural networks.Second,the multiscale feature extraction layers can powerfully extract dataset characteristics.Finally,the multiscale feature extraction layers with fully considered historical measurements can contain richer useful information and improved representation compared to traditional data-driven models.

Multi-scalefeature extractordeep neural network(DNN)multirate sampled industrial processesprediction

Dezheng Wang、Yinglong Wang、Fan Yang、Liyang Xu、Yinong Zhang、Yiran Chen、Ning Liao

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School of Automation,Southeast University,Nanjing 210096,China

Software and Artificial Intelligence College,Chongqing Institute of Engineering,Chongqing 400056,China

Beijing National Research Center for Information Science and Technology(BNRist),Department of Automation,Tsinghua University,Beijing 100084,China

Liangjiang International College,Chongqing University of Technology,Chongqing 401135,China

Smart City College,Beijing Union University,Beijing 100101,China

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National Natural Science Foundation of ChinaScience and Technology Research Program of the Chongqing Municipal Education Commission,ChinaScience and Technology Research Program of the Chongqing Municipal Education Commission,ChinaScience and Technology Research Program of the Chongqing Municipal Education Commission,ChinaScience and Technology Research Program of the Chongqing Municipal Education Commission,ChinaScience and Technology Research Program of the Chongqing Municipal Education Commission,ChinaScientific Research Foundation of Chongqing University of Technology,ChinaScientific Research Foundation of Chongqing Institute of Engineering,ChinaChongqing Municipal Natural Science Foundation,China

61873142KJZD-K202201901KJQN202201109KJQN202101904KJQN202001903CXQT210352019ZD762020xzky05cstc2020jcyjmsxmX0666

2024

10.1007/s11633-022-1401-9

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(2)
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