文章基本信息

标题：A Model Combining Seq2Seq Network and LightGBM Algorithm for Industrial Soft Sensor
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作者：Yanrui Li ; Chunjie Yang ; Hanwen Zhang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：12068-12073
DOI：10.1016/j.ifacol.2020.12.753
语种：English
出版社：Elsevier
摘要：AbstractAs a key technology for industry 4.0, data-driven soft sensing plays an important role in the control and optimization of industrial processes. However, due to the large-scale, nonlinear and dynamic characteristics of industrial data, it is difficult to process industrial data. To solve these difficulties, a soft sensor modeling method based on a sequence to sequence model and a gradient boosting tree algorithm is developed. In this method, an unsupervised trained Seq2Seq model is used to extract dynamic features at first. Then a high-precision model based on LightGBM is constructed with the dynamic features and the original features as inputs. The developed method is validated on pulping data and compared with other machine learning methods such as RNN and SVR. The result shows the developed method has a better performance.
关键词：KeywordsSoft sensingdynamic featuresequence to sequencedata integrationprocess industry