数据驱动的工业过程运行监控与自优化研究展望
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摘要
现代工业过程向大规模、连续化、集成化方向发展,有必要对生产全流程运行的决策、协同控制、底层控制进行有效监控,也是当前国际控制领域的研究热点.本文首先分析了工业过程全流程运行监控的内涵与行业现状;其次,阐述了基于模型的控制系统故障诊断与容错控制方法,以及数据驱动的异常工况诊断与自愈控制方法的研究现状,并指明了信息物理系统(Cyber-physical systems, CPS)时代智能安全运行监控与自优化的发展机遇;最后,论述了工业过程运行监控与自优化研究的新方向和最新进展,包括:1)数据驱动的决策、协同控制、底层控制多层面联合监控; 2)基于机理、数据、知识多源动态信息融合的异常工况诊断; 3)专家知识与控制手段相结合的协同层自愈控制; 4)数据驱动的运行动态性能分析与自优化; 5)支撑运行监控与自优化系统的实现技术.
With the development towards large-scale, continuous, and integrated modern industrial processes, it is essential to effectively monitor the plant-wide operations that cover decision, cooperative control, and base-level control. The operation monitoring has recently become an active area of research both in academia and industry. Firstly, the demanding work of operation monitoring and the current status in industrial area are analyzed in this paper. Secondly, the existing methods on model-based fault diagnosis and fault-tolerant control, and data driven abnormal situation diagnosis and self healing control are reviewed, while the opportunities are analyzed under cyber physical systems(CPS) circumstances.Finally, future research directions and recent progresses on the topic of operation monitoring and self-optimization of industrial processes are discussed, including: 1) data-driven multi-level comprehensive monitoring of decision, cooperative control, and base-level control; 2) multi-source dynamic information based abnormal situation diagnosis that combines first principles, process data, and expert knowledge; 3) cooperative self-healing control which combines expert knowledge and control strategy; 4) data driven dynamic performance analysis of process operation and self-optimization; and 5)technologies that implement operation monitoring and self-optimization system.
With the development towards large-scale, continuous, and integrated modern industrial processes, it is essential to effectively monitor the plant-wide operations that cover decision, cooperative control, and base-level control. The operation monitoring has recently become an active area of research both in academia and industry. Firstly, the demanding work of operation monitoring and the current status in industrial area are analyzed in this paper. Secondly, the existing methods on model-based fault diagnosis and fault-tolerant control, and data driven abnormal situation diagnosis and self healing control are reviewed, while the opportunities are analyzed under cyber physical systems(CPS) circumstances.Finally, future research directions and recent progresses on the topic of operation monitoring and self-optimization of industrial processes are discussed, including: 1) data-driven multi-level comprehensive monitoring of decision, cooperative control, and base-level control; 2) multi-source dynamic information based abnormal situation diagnosis that combines first principles, process data, and expert knowledge; 3) cooperative self-healing control which combines expert knowledge and control strategy; 4) data driven dynamic performance analysis of process operation and self-optimization; and 5)technologies that implement operation monitoring and self-optimization system.
引文
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2 Chai Tian-You. Operational optimization and feedback control for complex industrial processes. Acta Automatica Sinica, 2013, 39(11):1744-1757(柴天佑.复杂工业过程运行优化与反馈控制.自动化学报, 2013,39(11):1744-1757)
3 Zhang Y L, Dudzic M S. TIndustrial application of multivariate SPC to continuous caster start-up operations for breakout prevention. Control Engineering Practice, 2006,14(11):1357-1375
4 Zhang Y L, Dudzic M S. Online monitoring of steel casting processes using multivariate statistical technologies:from continuous to transitional operations. Journal of Process Control, 2006, 16(8):819-829
5 Isermann R, Ball′e P. Trends in the application of modelbased fault detection and diagnosis of technical processes.Control Engineering Practice, 1997, 5(5):709-719
6 Patton R J, Chen J. Observer-based fault detection and isolation:robustness and applications. Control Engineering Practice, 1997, 5(5):671-682
7 Zhou Dong-Hua, Hu Yan-Yan. Fault diagnosis techniques for dynamic systems. Acta Automatica Sinica, 2009, 35(6):748-758(周东华,胡艳艳.动态系统的故障诊断技术.自动化学报, 2009,35(6):748-758)
8 Li X J, Yang G H. Fault detection in finite frequency domain for Takagi-Sugeno fuzzy systems with sensor faults.IEEE Transactions on Cybernetics, 2014, 44(8):1446-1458
9 Wang H M, Yang G H, Ye D. Fault detection and isolation for affine fuzzy systems with sensor faults. IEEE Transactions on Fuzzy Systems, 2016, 24(5):1058-1071
10 Zhou Dong-Hua, Ding X. Theory and applications of fault tolerant control. Acta Automatica Sinica, 2000, 26(6):788-797(周东华, Ding X.容错控制理论及其应用.自动化学报, 2000,26(6):788-797)
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12 Zhang J X, Yang G H. Prescribed performance faulttolerant control of uncertain nonlinear systems with unknown control directions. IEEE Transactions on Automatic Control, 2017, 62(12):6529-6535
13 Qin S J. Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control, 2012,36(2):220-234
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15 Dunia R, Qin S J. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal,1998, 44(8):1813-1831
16 Alcala C F, Qin S J. Reconstruction-based contribution for process monitoring. Automatica, 2009, 45(7):1593-1600
17 Zhao C H, Gao F R. Fault subspace selection approach combined with analysis of relative changes for reconstruction modeling and multifault diagnosis. IEEE Transactions on Control Systems Technology, 2016, 24(3):928-939
18 Kano M, Tanaka S, Hasebe S, Hashimoto I, Ohno H. Monitoring independent components for fault detection. AIChE Journal, 2003, 49(4):969-976
19 Lee J M, Qin S J, Lee I B. Fault detection and diagnosis of multivariate processes based on modified independent component analysis. AIChE Journal, 2006, 52(10):3501-3514
20 Kano M, Hasebe S, Hashimoto I, Ohno H. Evolution of multivariate statistical process control:application of independent component analysis and external analysis.Computers and Chemical Engineering, 2004, 28(6-7):1157-1166
21 Liu Q, Chai T Y, Qin S J. Fault diagnosis of continuous annealing processes using a reconstruction-based method.Control Engineering Practice, 2012, 20(5):511-518
22 Zhao C H, Sun Y X. Subspace decomposition approach of fault deviations and its application to fault reconstruction.Control Engineering Practice, 2013, 21(10):1396-1409
23 Jiang Q C, Yan X F, Huang B. Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference. IEEE Transactions on Industrial Electronics, 2016, 63(1):377-386
24 Qin S J, Valle S, Piovoso M J. On unifying multiblock analysis with application to decentralized process monitoring.Journal of Chemometrics, 2001, 15(9):715-742
25 Cherry G A, Qin S J. Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis. IEEE Transactions on Semiconductor Manufacturing, 2006, 19(2):159-172
26 Zhu J L, Ge Z Q, Song Z H. Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data. IEEE Transactions on Industrial Informatics, 2017, 13(4):1877-1885
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