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
  • [1] MIAO Q, JIANG J, ZHANG H, Et al., Development of aviation intelligent engine under industrial big data: Chances and challenges, Chinese Journal of Scientific Instrument, 40, 7, pp. 1-12, (2019)
  • [2] YUAN X F, GE Z Q, HUANG B, Et al., A probabilistic just-in-time learning framework for soft sensor development with missing data, IEEE Transactions on Control Systems Technology, 25, 3, pp. 1124-1132, (2016)
  • [3] SHAO W M, GE Z Q, SONG Z H., Bayesian just-in-time learning and its application to industrial soft sensing, IEEE Transactions on Industrial Informatics, 16, 4, pp. 2787-2798, (2020)
  • [4] CHE X Q, XIONG W L., Weighted gaussian model soft sensor modeling and its application with time delay estimation, Chinese Journal of Scientific Instrument, 39, 9, pp. 195-202, (2018)
  • [5] CHEN Z W, LIU C, DING S X, Et al., A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection, IEEE Transactions on Industrial Electronics, 68, 6, pp. 5259-5270, (2021)
  • [6] XIONG W L, ZHANG W, XU B G, Et al., JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy, Computers & Chemical Engineering, 90, pp. 260-267, (2016)
  • [7] KE T, LYU H, SUN M J, Et al., A biased least squares support vector machine based on Mahalanobis distance for PU learning, Physica A: Statistical Mechanics and its Applications, 509, pp. 422-438, (2018)
  • [8] YUAN X F, GE Z Q, HUANG B, Et al., Semisupervised JITL framework for nonlinear industrial soft sensing based on locally semisupervised weighted PCR, IEEE Transactions on Industrial Informatics, 13, 2, pp. 532-541, (2016)
  • [9] XIA P P, ZHANG L, LI F Z., Learning similarity with cosine similarity ensemble, Information Sciences, 307, pp. 39-52, (2015)
  • [10] YANG X Y, WU J D, MA J., Rolling bearing performance degradation assessment method based on dispersion entropy and cosine Euclidean distance, Journal of Electronic Measurement and Instrumentation, 34, 7, pp. 15-24, (2020)
123