Journal of Shanghai Jiao Tong University ›› 2021, Vol. 55 ›› Issue (11): 1417-1428.doi: 10.16183/j.cnki.jsjtu.2020.290

Special Issue: 《上海交通大学学报》2021年12期专题汇总专辑 《上海交通大学学报》2021年“自动化技术、计算机技术”专题

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Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE

ZHAO Xiaoqianga,b,c(), MOU Miaoa   

  1. a. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    b. Key Laboratory of Advanced Control for Industrial Processes of Gansu Province, Lanzhou University of Technology, Lanzhou 730050, China
    c. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China
  • Received:2020-09-14 Online:2021-11-28 Published:2021-12-03


Traditional process monitoring methods ignore the time-series correlation between variables, and do not distinguish the dynamic relationship and static relationship between variables, resulting in poor monitoring effect. To solve these problems, a dynamic-static joint indicator monitoring method of batch process based on global slow feature analysis(GSFA)-global neighborhood preserving embedding (GNPE) is proposed in this paper, which can effectively extract dynamic global features and static global features. First, the dynamic and static characteristics of the process variables are evaluated. Variables with weak autocorrelation and cross-correlation are regarded as static variables, and the remaining variables are regarded as dynamic ones. Next, the GSFA and GNPE models are constructed for dynamic and static subspaces, respectively. Finally, the statistical information from each subspace is combined by using Bayesian inference to obtain the joint indicator of the mixed model to realize process monitoring. Finally, the proposed algorithm is applied to a numerical example and the penicillin fermentation simulation process for simulation verification. The results show that the proposed GSFA-GNPE algorithm has better fault detection effects than other algorithms.

Key words: batch process, process monitoring, slow feature analysis, neighborhood preserving embedding, globality-locality, Bayesian inference

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