基于黎曼度量的一类反馈控制系统性能监测与诊断

李琳琳; 李莎莎; DING Steven Xianchun; 彭鑫; 彭开香

doi:10.16383/j.aas.c210027

基于黎曼度量的一类反馈控制系统性能监测与诊断

doi: 10.16383/j.aas.c210027

李琳琳^{1, 2,},
李莎莎^1,,
DING Steven Xianchun^3,,
彭鑫^4,,
彭开香^{1, 2,}

1.
北京科技大学自动化学院北京 100083 中国
2.
北京科技大学工业过程知识自动化教育部重点实验室北京 100083 中国
3.
德国杜伊斯堡−埃森大学自动控制与复杂系统研究所杜伊斯堡 47057 德国
4.
华东理工大学能源化工过程智能制造教育部重点实验室上海 200237 中国

基金项目: 国家自然科学基金(62073029, U21A20483, 62003140), 北京市自然科学基金(4202045), 中央高校基本业务费(FRF-TP-20-012A3)资助

详细信息

作者简介:
李琳琳：北京科技大学自动化学院教授. 2008年获得西安交通大学学士学位, 2011年获得北京大学硕士学位, 2015 年获得德国杜伊斯堡−埃森大学博士学位. 主要研究方向为故障诊断与容错控制, 智能控制, 非线性系统的模糊控制和诊断. 本文通信作者. E-mail: linlin.li@ustb.edu.cn

李莎莎：北京科技大学自动化学院硕士研究生. 主要研究方向为故障诊断, 容错控制, 性能监测. E-mail: S20190578@xs.ustb.edu.cn

DING Steven Xianchun：德国杜伊斯堡−埃森大学控制工程教授, 自动控制与复杂系统研究所所长. 1992年于德国杜伊斯堡的Gerhard-Mercato大学获得博士学位. 主要研究方向为基于模型与数据驱动的故障诊断, 容错控制, 以及其在汽车系统和化工过程中的应用. E-mail: steven.ding@uni-due.de

彭鑫：华东理工大学能源化工过程智能制造教育部重点实验室研究员. 2009年和2017年分别获得华东理工大学控制科学与工程学士及博士学位. 主要研究方向为工业过程智能建模, 控制及优化, 数据驱动的状态监测与溯源诊断. E-mail: xinpeng@ecust.edu.cn

彭开香：北京科技大学自动化学院教授. 2007 年获得北京科技大学控制科学与工程博士学位. 主要研究方向为复杂工业系统故障诊断与容错控制. E-mail: kaixiang@ustb.edu.cn

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出版历程
- 收稿日期: 2021-01-09
- 录用日期: 2021-05-12
- 网络出版日期: 2021-06-15
- 刊出日期: 2023-09-26

Riemannian Metric Based Performance Monitoring and Diagnosis for a Class of Feedback Control Systems

LI Lin-Lin^{1, 2
,},
LI Sha-Sha^1
,,
DING Steven Xianchun^3
,,
PENG Xin^4
,,
PENG Kai-Xiang^{1, 2
,}

1.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing 100083, China
3.
Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg 47057, Germany
4.
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Funds: Supported by National Natural Science Foundation of China (62073029, U21A20483, 62003140), Beijing Natural Science Foundation (4202045), and the Fundamental Research Funds for the Central Universities (FRF-TP-20-012A3)

More Information

Author Bio:
LI Lin-Lin　Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. She received her bachelor degree from Xi＇an Jiaotong University in 2008 and her master degree from Peking University in 2011. In 2015, she received her Ph.D. degree at the Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany. Her research interest covers fault diagnosis and fault tolerant control, intelligent control, fuzzy control and estimation for nonlinear systems. Corresponding author of this paper

LI Sha-Sha　Master student at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. Her research interest covers fault diagnosis, fault-tolerant control, and performance monitoring

DING Steven Xianchun　Professor of control engineering and the head of the Institute for Automatic Control and Complex Systems (AKS) at the University of Duisburg-Essen, Germany. He received his Ph.D. degree from the Gerhard-Mercator University of Duisburg, Germany, in 1992. His research interest covers model-based and data-driven fault diagnosis, fault tolerant systems, and their application in industry with a focus on automotive systems and chemical processes

PENG Xin　Researcher at the Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology. He received his bachelor degree and Ph.D. degree in control science and engineering from East China University of Science and Technology in 2009 and 2017, respectively. His research interest covers modeling, optimization and control of complex industrial process, and data-driven process monitoring and traceable diagnosis

PENG Kai-Xiang　Professor at the School of Automation and Electrical Engineering, University of Science and Technology Beijing. He received his Ph.D. degree in control science and engineering from University of Science and Technology Beijing in 2007. His research interest covers fault diagnosis and fault-tolerant control for complex industrial system

摘要

摘要: 针对复杂工业系统对性能衰退的容忍度低等问题, 提出基于系统性能预测的一类反馈控制系统过程监测方法, 通过黎曼度量对控制性能衰退程度进行预测与监测, 并给出发生故障的类型, 以提升过程监测系统的实时性与准确性. 首先, 利用系统的实时数据, 计算系统性能衰退的预测指标; 其次, 利用黎曼度量对系统性能衰退程度进行预测与监测, 并利用随机算法给出对应的阈值来诊断系统性能衰退; 最后, 通过计算各类引发系统性能衰退的故障的性能预测指标集合的中心和阈值, 实现故障的实时定位. 所提出的方法通过三容水箱仿真实验平台进行验证.
- 性能监测 /
- 黎曼度量 /
- 性能诊断 /
- 故障定位
Abstract: Associated with the issue that the industrial processes have low tolerance in performance degradation, a performance monitoring and diagnosis scheme for a class of feedback control systems is developed based on system performance prediction. The Riemannian metric is then adopted to predict and monitor the degree of the control performance degradation, and classify the fault type, so as to improve the real-time ability and accuracy of the performance monitoring systems. Firstly, a prediction indicator is introduced to predict the system performance degradation using online process data. Then, the Riemannian metric is implemented to predict and monitor the degree of the control performance degradation, and the randomized algorithm is adopted to set the threshold for diagnosing the performance degradation. Finally, by computing the center and threshold for the performance predictor set caused by all the possible faults, the fault localization can be achieved. The effectiveness of the proposed method is verified by a case study on a three-tank system.
- Performance monitoring /
- Riemannian metric /
- performance diagnosis /
- fault location

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图 1 基于黎曼度量的变化检测

Fig. 1 Riemannian metric based change detection

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图 2 基于黎曼度量的控制性能监测流程图

Fig. 2 Flow chart of Riemannian metric based control performance monitoring

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图 3 三容水箱示意图

Fig. 3 The schematic of the three-tank system

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图 4 管道1发生堵塞时诊断效果图

Fig. 4 Detection performance of plugging in Pipe 1

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图 5 管道2发生堵塞时诊断效果图

Fig. 5 Detection performance of plugging in Pipe 2

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图 6 管道3发生堵塞时诊断效果图

Fig. 6 Detection performance of plugging in Pipe 3

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图 7 控制参数不匹配时诊断效果图

Fig. 7 Detection performance of controller parameter unmatch

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图 8 基于ISPD的性能检测结果^[16]

Fig. 8 ISPD based performance detection results^[16]

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表 1 水箱DTS200的参数

Table 1 Parameters of tank DTS200

参数	符号	值	单位
水箱面积	$ {\cal{A}} $	154	$ \mathrm{cm}^{2} $
水箱间管道面积	$s_{{n} }$	0.5	$ \mathrm{cm}^{2} $
水箱最高水位	$ H_{\mathrm{max}} $	62	$ \mathrm{cm} $
泵 1 的最大进水量	$ Q_{1_{\mathrm{max}}} $	120	$ \mathrm{cm}^{3}/\mathrm{s} $
泵 2 的最大进水量	$ Q_{2_{\mathrm{max}}} $	120	$ \mathrm{cm}^{3}/\mathrm{s} $
管道 1 水流系数	$ a_{1} $	0.45
管道 2 水流系数	$ a_{2} $	0.60
管道 3 水流系数	$ a_{3} $	0.45

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表 2 水箱堵塞故障隔离

Table 2 Isolation of pipe plugging

故障	$ d_R^2({\boldsymbol P},{\boldsymbol P}_{z,1}) $	$ d_R^2({\boldsymbol P},{\boldsymbol P}_{z,2}) $	$ d_R^2({\boldsymbol P},{\boldsymbol P}_{z,3}) $	故障隔离
$a_1 = 0.30$	$ 0.0388 $	$ 0.1088 $	$ 0.1193 $	故障簇 1
$ a_2 = 0.35 $	$ 0.0894 $	$ 0.0270 $	$ 0.0810 $	故障簇 2
$ a_3 = 0.28 $	$ 0.1258 $	$ 0.1099 $	$ 0.0478 $	故障簇 3
$ a_1 = 0.27 $	$ 0.0636 $	$ 0.1325 $	$ 0.1409 $	故障簇 1
$a_2 = 0.40$	$ 0.0809 $	$ 0.0139 $	$ 0.0732 $	故障簇 2

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