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

作　　者：Dezheng Wang Yinglong Wang Fan Yang Liyang Xu Yinong Zhang Yiran Chen Ning Liao 

机构地区：[1]School of Automation,Southeast University,Nanjing 210096,China [2]Software and Artificial Intelligence College,Chongqing Institute of Engineering,Chongqing 400056,China [3]Beijing National Research Center for Information Science and Technology(BNRist),Department of Automation,Tsinghua University,Beijing 100084,China [4]Liangjiang International College,Chongqing University of Technology,Chongqing 401135,China [5]Smart City College,Beijing Union University,Beijing 100101,China

出　　处：《Machine Intelligence Research》2024年第2期400-410,共11页机器智能研究（英文版）

基　　金：supported by National Natural Science Foundation of China(No.61873142);the Science and Technology Research Program of the Chongqing Municipal Education Commission,China(Nos.KJZD-K202201901,KJQN202201109,KJQN202101904,KJQN202001903 and CXQT21035);the Scientific Research Foundation of Chongqing University of Technology,China(No.2019ZD76);the Scientific Research Foundation of Chongqing Institute of Engineering,China(No.2020xzky05);the Chongqing Municipal Natural Science Foundation,China(No.cstc2020jcyj-msxmX0666).

摘　　要：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 because it provides a reliable and stable online estimation of such variables.This paper employs a deep neural network with multiscale feature 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 advantages 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.

关 键 词：MULTI-SCALE feature extractor deep neural network(DNN) multirate sampled industrial processes prediction 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]