基于动态多属性决策方法的工业过程控制性能评价

doi:10.11949/j.issn.0438-1157.20180259

摘要/Abstract

摘要：

工业控制系统由于设备老化而导致控制性能发生变化，而传统的控制性能评价方法对于这种慢时变特性具有局限性。提出了一种基于变权动态多属性决策的控制性能评价方法。首先在对系统性能进行判断时，利用系统故障率与运行时间相结合的计算方法对系统的运行状态进行划分，获得不同运行阶段的决策信息；采用四个评价指标分别为超调量、非线性指标、输出方差、阀黏滞指标构造多属性决策判断矩阵，对老化慢时变系统进行评价，并运用色谱分析法计算决策过程中属性权重的变化，最终确定当前性能运行状态；将该方法应用于某工业DMF回收装置中，对比实验结果验证了该方法的有效性。

关键词: 动态多属性决策, 过程系统性能评价, 系统老化, 数据集成

Abstract:

Practically, the aging of industrial equipments could lead to varying performances of control systems, while traditional control performance evaluation methods show limitations in this scenario. In response, a control system performance adaptive evaluation approach based on variable weight dynamic multi-attribute decision-making is proposed. Firstly, to judge the control performance, a calculation method that combines the system failure rate and operating time is suggested to divide the operating state of control systems to ensure that the decision information comes from different operation stages. Subsequently, a multi-attribute decision-making matrix is constructed with four evaluation indicators including the overshoot, nonlinear index, output variance, and valve stick index to evaluate aging slow time-varying systems. An analytic hierarchy process method is used to calculate the change of the attribute weight in the decision process to determine the current performance state. The proposed method was applied to an industrial DMF recovery plant, demonstrating the effectiveness of the method.

Key words: dynamic multi-attribute decision making, control performance assessment, aging system, data integration

中图分类号:

TP277

罗琳, 杨博, 李宏光. 基于动态多属性决策方法的工业过程控制性能评价[J]. 化工学报, 2018, 69(S1): 87-94.

LUO Lin, YANG Bo, LI Hongguang. A dynamic multi-attribute decision making approach to industrial process control performance evaluations[J]. CIESC Journal, 2018, 69(S1): 87-94.

参考文献 32

[1]	VERONESI M,VISIOLI A. Performance assessment and retuning of PID controllers for integral processes[J]. Journal of Process Control, 2010, 20(3):261-269.
[2]	BIALKOWSKI W L. Dreams versus reality:a view from both sides of the gap[J]. Pulp & Paper Canada, 1993, 94(11):19-27.
[3]	JELALI M. Control system performance monitoring[J]. Methoden und Anwendungen der Steuerungs-, Regelungs-und Informationstechnik, 2010, 58(5):286-288.
[4]	HARRIS T J. Assessment of control loop performance[J]. Canadian Journal of Chemical Engineering, 1989, 67(5):856-861.
[5]	DESBOROUGH L,HARRIS T. Performance assessment measures for univariate feed forward feedback control[J].Canadian Journal of Chemical Engineering,1993,71(4):1186-1197.
[6]	KO B S,EDGER T E. Performance assessment of cascade control loops[J].AIChE Journal,2000,46(2):281-291.
[7]	HUANG B,SHAH S L,FUJⅡ H. The unitary interactor matrix and its estimation using closed-loop data[J].Journal of Process Control,1997,7(3):95-207.
[8]	THORNHILL N F, OETTINGER M, FEDENCZUK P. Refinery-wide control loop performance assessment[J]. Journal of Process Control, 1999, 9(1999):109-124.
[9]	张荣金, 张泉灵. 基于历史性能基准的模型预测控制性能监控[J]. 计算机与应用化学, 2011, 28(3):275-278. ZHANG R J,ZHANG Q L. Model predictive control performance monitoring based on historical performance benchmarks[J]. Computers and Applied Chemistry,2011, 28(3):275-278.
[10]	SHANG L, TIAN X, CAI L. A multi-index control performance assessment method based on historical prediction error covariance[J]. International Federation of Automatic Control,2017, 50(1):13892-13897.
[11]	KHAMSEH S A, SEDIGH A K, MOSHIRI B, et al. Control performance assessment based on sensor fusion techniques[J]. Control Engineering Practice, 2016, 49:14-28.
[12]	GAO X, YANG F, SHANG C, et al. A review of control loop monitoring and diagnosis:prospects of controller maintenance in big data era[J]. Chinese Journal of Chemical Engineering, 2016, 24(8):952-962.
[13]	JELALI M. Diagnosis of stiction-related actuator problems[M]//Advances in Industrial Control. London:Springer,2013:265-300.
[14]	CHOUDHURY M A A S, THORNHILL N F, SHAH S L. Modelling valve stiction[J]. Control Engineering Practice, 2005, 13(5):641-658.
[15]	YAMASSHITA Y. Qualitative Analysis for Detection of Stiction in Control Valves[M]. Berlin:Springer Berlin Heidelberg, 2004:391-397.
[16]	王培宇. 工业控制回路非线性检测研究[D]. 杭州:浙江大学, 2015. WANG P Y. Research on nonlinear detection of industrial control loops[D].Hangzhou:Zhejiang University, 2015.
[17]	PATWARDHAN R S, SHAH S L, QI K Z. Assessing the performance of model predictive controllers[J]. Canadian Journal of Chemical Engineering, 2002, 80(5):954-966.
[18]	SCHAFER J,CINAR A. Multivariable MPC system performance assessment, monitoring, and diagnosis[J]. Process Control, 2004,14(2):113-129.
[19]	张敏敏. 基于模型预测控制的动态多属性决策方法[D]. 北京:北京化工大学,2016. ZHANG M M. Dynamic multi-attribute decision-making method based on model predictive control[D]. Beijing:Beijing University of Chemical Technology, 2016
[20]	WANG Z, HAN Y, GENG Z, et al. PID control loop performance assessment and diagnosis based on DEA-related MCDA[C]//International Symposium on Advanced Control of Industrial Processes.New York:IEEE, 2017:535-540.
[21]	WEI G. Grey relational analysis model for dynamic hybrid multiple attribute decision making[J]. Knowledge-Based Systems, 2011, 24(5):672-679.
[22]	CHEN Y, LI B. Dynamic multi-attribute decision making model based on triangular intuitionistic fuzzy numbers[J]. Scientia Iranica, 2011, 18(2):268-274.
[23]	SU X, LI H, JIANG H. Internet of things decision technology based on dynamic multi-attibute decision making[C]//International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things. IET, 2014:216-220.
[24]	AL-HARBI A S. Application of the AHP in project management[J]. International Journal of Project Management, 2001, 19(1):19-27.
[25]	邓雪, 李家铭, 曾浩健,等. 层次分析法权重计算方法分析及其应用研究[J]. 数学的实践与认识, 2012, 42(7):93-100. DENG X,LI J M,ZENG H J,et al. Analytic hierarchy process weight calculation method analysis and application research[J]. Mathematics in Practice and Theory,2012,42(7):93-100.
[26]	ZHI F, TIAN Y, QUAN Z. The sensitivity analysis to attribute values based on the additive weighting model in multiple attribute decision making[J]. Journal of Northeastern University, 2002, 23(1):83-86.
[27]	YAGER R R. On ordered weighted averaging aggregation operators in multi-criteria decision making[J]. Readings in Fuzzy Sets for Intelligent Systems, 1993, 18(1):80-87.
[28]	CHOUDHURY M A A S, SHAH S L,THORNHILL N F. Diagnosis of poor control-loop performance using higher-order statistics[J]. Automatica, 2004, 40(10):1719-1728.
[29]	韩俊林, 王树青, 谢磊,等. 一种控制阀黏滞特性快速检测方法研究[J]. 化工自动化及仪表, 2010, 37(3):47-51. HAN J L,WANG S Q,XIE L, et al. A study on a rapid detection method of viscous characteristics of control valves[J]. Control and Instruments in Chemical Industry,2010,37(3):47-51.
[30]	王慧芳, 杨荷娟, 何奔腾,等. 输变电设备状态故障率模型改进分析[J]. 电力系统自动化, 2011, 35(16):27-31. WANG H F,YANG H J,HE B T,et al. Improvement analysis of state failure rate model for power transmission equipment[J].Automation of Electric Power Systems,2011,35(16):27-31.
[31]	魏鹏程. DMF回收设备的节能探讨[J]. 聚氨酯工业, 2002, 17(4):43-46. WEI P C. Discussion on energy saving of DMF recovery equipment[J]. Polyurethane Industry,2002,17(4):43-46.
[32]	齐良才, 李宏光. 基于数据驱动的控制器性能多属性评价方法[J]. 华东理工大学学报, 2014, 40(2):244-250. QI L C,LI H G. A multi-attribute assessment method of controller performance based on data-driven[J]. Journal of East China University of Science and Technology,2014,40(2):244-250.

编辑推荐

Metrics

阅读次数

全文

221

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	221

来源	本网站	其他网站

次数	118	103
比例	53%	47%

摘要

376

最新录用	在线预览	正式出版

0	0	376

来源	本网站	其他网站

次数	241	135
比例	64%	36%