Online modeling of just-in-time learning based on spatial-temporal similarity

被引：0
|
作者：
Shi J. [1 ,2 ]
Chen L. [1 ,2 ]
Qin K. [1 ,2 ]
Li Z. [1 ,2 ]
Hao K. [1 ,2 ]
机构：
[1] Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, Donghua University, Shanghai
[2] College of Information Science and Technology, Donghua University, Shanghai
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2022年 / 43卷 / 06期
关键词：
Data-driven; Just-in-time learning; Online modeling; Process industry; Spatial-temporal similarity;
D O I：
10.19650/j.cnki.cjsi.J2209166
中图分类号：
学科分类号：
摘要：
Data in the process industry are highly time-varying and nonlinear. Traditional offline models can hardly cope with the changing working conditions in the actual production process, while the just-in-time learning (JITL) is an effective online modeling method. Most of the studied similarity measurements of JITL only focus on samples' spatial distance, which ignore the time-series characteristics of industrial data. To address this issue, a JITL method based on spatial-temporal similarity is proposed. First, the sample point is extended into a sample sequence, and the temporal-sequence distance among samples is calculated by combining dynamic time warping. Then, the spatial-temporal similarity metric (SSM) is proposed, and the SSM is constructed by nonlinearly weighting the temporal and spatial distances. Finally, the online modeling method for just-in-time learning based on spatial-temporal similarity (SS-JITL) is proposed. The algorithm is applied to a public dataset and an actual polyester fiber polymerization process. Experiment results show that the goodness of fit reaches 91.6% and 98.6%, which demonstrates the effectiveness and superiority of the proposed algorithm. © 2022, Science Press. All right reserved.
引用
收藏
页码：185 / 193
页数：8
相关论文
共 24 条
  • [21] YANG Z Y, GE Z Q., Rethinking the value of just-in-time learning in the era of industrial big data, IEEE Transactions on Industrial Informatics, 18, 2, pp. 976-985, (2022)
  • [22] DE VITO S, MASSERA E, PIGA M, Et al., On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, 129, 2, pp. 750-757, (2008)
  • [23] WANG J H, XU S, WANG Y H, Et al., Prepolymerization to final polycondensation process optimization of Du Pont three reactors plant, Polyester Industry, 25, 2, pp. 30-31, (2012)
  • [24] QI CH, XIONG W L., A just-in-time learning soft sensing modeling method based on bayesian gaussian mixture model, Journal of System Simulation, 31, 8, pp. 1555-1561, (2019)
123