Data-driven Robust Evaluation Method for Optimal Operating Status and Its Application

被引：0
|
作者：
Chu F. [1 ,2 ]
Zhao X. [1 ,2 ]
Dai W. [2 ]
Ma X.-P. [2 ]
Wang F.-L. [3 ,4 ]
机构：
[1] Research Center of Underground Space Intelligent Control Engineering of the Ministry of Education, China University of Mining and Technology, Xuzhou
[2] School of Information and Control Engineering, China University of Mining and Technology, Xuzhou
[3] College of Information Science and Engineering, Northeastern University, Shenyang
[4] State Key Laboratory of Integrated Automation for Process Industries, Northeastern University, Shenyang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Complex industrial process; Data-driven; Non-optimal factor; Operational status evaluation; Total partial robust M-regression;
D O I：
10.16383/j.aas.c180018
中图分类号：
学科分类号：
摘要：
In the process of modern complex industrial production, a detailed and robust evaluation method of operation state is of great significance for guiding the production. Considering the difficulty to establish an accurate principle model and the process data which are easily polluted by noise and outliers, this paper proposes a robust optimal evaluation method for complex industrial processes based on total partial robust M-regression. In the off-line modeling stage, by further decomposing the principal and residual subspaces of the process data, the process variation information related to the economic indexes reflecting the factors such as raw materials, production consumption and product quality is extracted, and the adverse effects of the outliers are eliminated by sample data weighting to improve the robustness of the algorithm. In the stage of online evaluation, the online data window and similarity analysis are introduced for the uncertain factors of the production process, and the framework and procedure of online evaluation are given to improve the reliability of the evaluation results. If the evaluation results are not optimal, then the non-optimal factors are identified by calculating the contribution rates of the corresponding variables. Finally, the effectiveness of the proposed method is illustrated by a process of dense medium coal preparation. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
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页码：439 / 450
页数：11
相关论文
共 38 条
  • [1] Wen Chen-Lin, Lv Fei-Ya, Bao Zhe-Jing, Liu Mei-Qin, A review of data driven-based incipient fault diagnosis, Acta Automatica Sinica, 42, 9, pp. 1285-1299, (2016)
  • [2] Yin S, Gao H J, Qiu J B, Kaynak O., Fault detection for nonlinear process with deterministic disturbances: a just-in-time learning based data driven method, IEEE Transactions on Cybernetics, 47, 11, pp. 3649-3657, (2017)
  • [3] del Rio-Chanona E A, Zhang D D, Vassiliadis V S., Model-based real-time optimisation of a fed-batch cyanobacterial hydrogen production process using economic model predictive control strategy, Chemical Engineering Science, 142, pp. 289-298, (2016)
  • [4] Rossi F, Copelli S, Colombo A, Pirola C, Manenti F., Online model-based optimization and control for the combined optimal operation and runaway prediction and prevention in (fed-)batch systems, Chemical Engineering Science, 138, pp. 760-771, (2015)
  • [5] Ji H Q, He X, Shang J, Zhou D H., Incipient fault detection with smoothing techniques in statistical process monitoring, Control Engineering Practice, 62, pp. 11-21, (2017)
  • [6] Frangos M., Uncertainty quantification for cuttings transport process monitoring while drilling by ensemble Kalman filtering, Journal of Process Control, 53, pp. 46-56, (2017)
  • [7] Liu Yang, Zhang Guo-Shan, Chemical process monitoring and fault diagnosis based on sensitive sparse principal component analysis, Control and Decision, 31, 7, pp. 1213-1218, (2016)
  • [8] Zou X Y, Wang F L, Chang Y Q, Zhang B., Process operating performance optimality assessment and non-optimal cause identification under uncertainties, Chemical Engineering Research and Design, 120, pp. 348-359, (2017)
  • [9] Mosallam A, Medjaher K, Zerhouni N., Component based data-driven prognostics for complex systems: methodology and applications, Proceedings of the 1st International Conference on Reliability Systems Engineering (ICRSE), pp. 1-7, (2015)
  • [10] Jia X D, Zhao M, Di Y, Yang Q B, Lee J., Assessment of data suitability for machine prognosis using maximum mean discrepancy, IEEE Transactions on Industrial Electronics, 65, 7, pp. 5872-5881, (2018)
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