基于GSFA-GNPE的动态-静态联合指标间歇过程监控

doi:10.16183/j.cnki.jsjtu.2020.290

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (11): 1417-1428.doi: 10.16183/j.cnki.jsjtu.2020.290

所属专题：《上海交通大学学报》2021年12期专题汇总专辑；《上海交通大学学报》2021年“自动化技术、计算机技术”专题

基于GSFA-GNPE的动态-静态联合指标间歇过程监控

赵小强^a^,^b^,^c(), 牟淼^a

a.电气工程与信息工程学院, 兰州理工大学, 兰州 730050
b.甘肃省工业过程先进控制重点实验室, 兰州理工大学, 兰州 730050
c.电气与控制工程国家级实验教学示范中心, 兰州理工大学, 兰州 730050

收稿日期:2020-09-14 出版日期:2021-11-28 发布日期:2021-12-03
作者简介:赵小强(1969-),男,陕西省宝鸡市人,教授,博士生导师,研究方向为过程监控和故障诊断、生产调度及数据挖掘.E-mail: xqzhao@lut.edu.cn.
基金资助:
国家自然科学基金(61763029);国防基础科研(JCKY2018427C002);甘肃省高等学校产业支撑引导(2019C-05);甘肃省工业过程先进控制重点实验室开放基金资助项目(2019KFJJ01)

Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE

ZHAO Xiaoqiang^a^,^b^,^c(), MOU Miao^a

a. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
b. Key Laboratory of Advanced Control for Industrial Processes of Gansu Province, Lanzhou University of Technology, Lanzhou 730050, China
c. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, China

Received:2020-09-14 Online:2021-11-28 Published:2021-12-03

摘要/Abstract

摘要：

传统的过程监控方法忽略了变量间的时序相关性,且没有区分变量间的动态关系与静态关系,从而导致监控效果不佳.针对此问题,本文提出一种基于全局慢特征分析(GSFA)-全局邻域保持嵌入(GNPE)的动态-静态联合指标间歇过程监控方法,该方法可以有效提取动态全局特征和静态全局特征.首先,对过程变量的动态特性和静态特性进行评估,把自相关和互相关性较弱的变量视为静态变量,剩余变量视为动态变量;其次,分别对动态子空间和静态子空间构建GSFA和GNPE模型;然后,对来自每个子空间的统计信息使用贝叶斯推理进行组合,以得出混合模型的联合指标实现过程监控;最后,将所提算法应用于数值算例和青霉素发酵仿真过程进行仿真验证.结果表明,GSFA-GNPE算法相较于其他算法的故障检测效果更好.

关键词: 间歇过程, 过程监控, 慢特征分析, 邻域保持嵌入, 全局-局部, 贝叶斯推断

Abstract:

Traditional process monitoring methods ignore the time-series correlation between variables, and do not distinguish the dynamic relationship and static relationship between variables, resulting in poor monitoring effect. To solve these problems, a dynamic-static joint indicator monitoring method of batch process based on global slow feature analysis(GSFA)-global neighborhood preserving embedding (GNPE) is proposed in this paper, which can effectively extract dynamic global features and static global features. First, the dynamic and static characteristics of the process variables are evaluated. Variables with weak autocorrelation and cross-correlation are regarded as static variables, and the remaining variables are regarded as dynamic ones. Next, the GSFA and GNPE models are constructed for dynamic and static subspaces, respectively. Finally, the statistical information from each subspace is combined by using Bayesian inference to obtain the joint indicator of the mixed model to realize process monitoring. Finally, the proposed algorithm is applied to a numerical example and the penicillin fermentation simulation process for simulation verification. The results show that the proposed GSFA-GNPE algorithm has better fault detection effects than other algorithms.

Key words: batch process, process monitoring, slow feature analysis, neighborhood preserving embedding, globality-locality, Bayesian inference

中图分类号:

TP277

赵小强, 牟淼. 基于GSFA-GNPE的动态-静态联合指标间歇过程监控[J]. 上海交通大学学报, 2021, 55(11): 1417-1428.

ZHAO Xiaoqiang, MOU Miao. Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE[J]. Journal of Shanghai Jiao Tong University, 2021, 55(11): 1417-1428.

图/表 10

图1

图2

表1

参考文献 24

[1]	WANG R, EDGAR T F, BALDEA M, et al. A geometric method for batch data visualization, process monitoring and fault detection[J]. Journal of Process Control, 2018, 67:197-205. doi: 10.1016/j.jprocont.2017.05.011 URL
[2]	赵春晖, 余万科, 高福荣. 非平稳间歇过程数据解析与状态监控——回顾与展望[J]. 自动化学报, 2020, 46(10):2072-2091.
	ZHAO Chunhui, YU Wanke, GAO Furong. Data analytics and condition monitoring methods for nonstationary batch processes—Current status and future[J]. Acta Automatica Sinica, 2020, 46(10):2072-2091.
[3]	纪洪泉, 何潇, 周东华. 基于多元统计分析的故障检测方法[J]. 上海交通大学学报, 2015, 49(6):842-848.
	JI Hongquan, HE Xiao, ZHOU Donghua. Fault detection techniques based on multivariate statistical analysis[J]. Journal of Shanghai Jiao Tong University, 2015, 49(6):842-848.
[4]	李晗, 萧德云. 基于数据驱动的故障诊断方法综述[J]. 控制与决策, 2011, 26(1):1-9.
	LI Han, XIAO Deyun. Survey on data driven fault diagnosis methods[J]. Control and Decision, 2011, 26(1):1-9.
[5]	姚旭, 王晓丹, 张玉玺, 等. 特征选择方法综述[J]. 控制与决策, 2012, 27(2):161-166.
	YAO Xu, WANG Xiaodan, ZHANG Yuxi, et al. Summary of feature selection algorithms[J]. Control and Decision, 2012, 27(2):161-166.
[6]	童楚东, 蓝艇, 史旭华. 基于互信息的分散式动态PCA故障检测方法[J]. 化工学报, 2016, 67(10):4317-4323.
	TONG Chudong, LAN Ting, SHI Xuhua. Fault detection by decentralized dynamic PCA algorithm on mutual information[J]. CIESC Journal, 2016, 67(10):4317-4323.
[7]	HE X F, CAI D, YAN S C, et al. Neighborhood preserving embedding[C]// Tenth IEEE International Conference on Computer Vision (ICCV’05) . Beijing: IEEE, 2005: 1208-1213.
[8]	陈法法, 杨晓青, 陈保家, 等. 基于正交邻域保持嵌入与多核相关向量机的滚动轴承早期故障诊断[J]. 计算机集成制造系统, 2018, 24(8):1946-1954.
	CHEN Fafa, YANG Xiaoqing, CHEN Baojia, et al. Early fault diagnosis of rolling bearing based on orthogonal neighbourhood preserving embedding and multi-kernel relevance vector machine[J]. Computer Integrated Manufacturing Systems, 2018, 24(8):1946-1954.
[9]	TONG C D, LAN T, SHI X H. Fault detection and diagnosis of dynamic processes using weighted dynamic decentralized PCA approach[J]. Chemometrics and Intelligent Laboratory Systems, 2017, 161:34-42. doi: 10.1016/j.chemolab.2016.11.015 URL
[10]	KU W F, STORER R H, GEORGAKIS C. Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1):179-196. doi: 10.1016/0169-7439(95)00076-3 URL
[11]	苗爱敏, 葛志强, 宋执环, 等. 基于时序扩展的邻域保持嵌入算法及其在故障检测中的应用[J]. 华东理工大学学报(自然科学版) , 2014, 40(2):218-224.
	MIAO Aimin, GE Zhiqiang, SONG Zhihuan, et al. Neighborhood preserving embedding based on temporal extension and its application in fault detection[J]. Journal of East China University of Science and Technology (Natural Science Edition) , 2014, 40(2):218-224.
[12]	ZHANG H Y, TIAN X M, DENG X G. Batch process monitoring based on multiway global preserving kernel slow feature analysis[J]. IEEE Access, 2017, 5:2696-2710. doi: 10.1109/ACCESS.2017.2672780 URL
[13]	HONG H F, JIANG C, PENG X, et al. Concurrent monitoring strategy for static and dynamic deviations based on selective ensemble learning using slow feature analysis[J]. Industrial & Engineering Chemistry Research, 2020, 59(10):4620-4635. doi: 10.1021/acs.iecr.9b05547 URL
[14]	YAN S F, JIANG Q C, ZHENG H Y, et al. Quality-relevant dynamic process monitoring based on dynamic total slow feature regression model[J]. Measurement Science and Technology, 2020, 31(7):075102. doi: 10.1088/1361-6501/ab7bbd URL
[15]	CHAI Z, ZHAO C H. Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification[J]. IEEE Transactions on Industrial Informatics, 2020, 16(1):54-66. doi: 10.1109/TII.9424 URL
[16]	WISKOTT L, SEJNOWSKI T J. Slow feature analysis: Unsupervised learning of invariances[J]. Neural Computation, 2002, 14(4):715-770. doi: 10.1162/089976602317318938 URL
[17]	索寒生, 蒋白桦, 宫向阳, 等. 基于SFA的工业过程质量相关的在线故障检测[J]. 控制工程, 2019, 26(6):1222-1227.
	SUO Hansheng, JIANG Baihua, GONG Xiangyang, et al. Online quality-related fault detection of industrial processes based on SFA[J]. Control Engineering of China, 2019, 26(6):1222-1227.
[18]	张汉元, 田学民. 基于KSFDA-SVDD的非线性过程故障检测方法[J]. 化工学报, 2016, 67(3):827-832.
	ZHANG Hanyuan, TIAN Xuemin. Nonlinear process fault detection based on KSFDA and SVDD[J]. CIESC Journal, 2016, 67(3):827-832.
[19]	SHARDT Y A W. Statistics for chemical and process engineers[M]. Berlin, Germany: Springer, 2015.
[20]	顾幸生, 周冰倩. 基于LNS-DEWKECA算法的多模态工业过程故障检测[J]. 控制与决策, 2020, 35(8):1879-1886.
	GU Xingsheng, ZHOU Bingqian. Multimodal industrial process fault detection based on LNS-DEWKECA[J]. Control and Decision, 2020, 35(8):1879-1886.
[21]	LEE J M, YOO C, LEE I B. Statistical process monitoring with independent component analysis[J]. Journal of Process Control, 2004, 14(5):467-485. doi: 10.1016/j.jprocont.2003.09.004 URL
[22]	ROBINSON J A. Probability and statistics for engineers and scientists[J]. Technometrics, 1990, 32(3):348-349.
[23]	胡永兵, 高学金, 李亚芬, 等. 批次加权软化分的多阶段AR-PCA间歇过程监测[J]. 仪器仪表学报, 2015, 36(6):1291-1300.
	HU Yongbing, GAO Xuejin, LI Yafen, et al. Multiphase AR-PCA monitoring for batch processes based on the batch weighted soft classifying[J]. Chinese Journal of Scientific Instrument, 2015, 36(6):1291-1300.
[24]	ZHAO H T. Dynamic graph embedding for fault detection[J]. Computers & Chemical Engineering, 2018, 117:359-371. doi: 10.1016/j.compchemeng.2018.05.018 URL

变量	$ρ i max$	变量	$ρ i max$
y₁	0.798	u₂	0.798
y₂	0.803	k₁	0.053
y₃	0.600	k₂	0.093
u₁	0.827	k₃	0.100

故障序号	NPE		SFA		GSFA-GNPE
故障序号	T²	SPE	S²	SPE	BIC-C²	BIC-SPE	BIC
1	0.905/0.190	0.850/0.110	0.795/0.185	0.794/0.160	0.985/0.065	0.920/0.105	0.995/0.095
2	0.75/0.16	0.925/0.085	0.630/0.145	0.63/0.12	0.835/0.145	0.895/0.060	0.945/0.065

序号	过程变量	序号	过程变量
1	通风速率	6	溶解氧浓度
2	搅拌功率	7	反应器体积
3	基质馈送率	8	二氧化碳浓度
4	补料温度	9	pH值
5	基质浓度	10	发酵罐温度

故障序号	变量序号	故障类型	幅值/%	引入时间/h
1	1	阶跃	5	200~400
2	2	斜坡	10	200~400
3	2	阶跃	5	200~400
4	1	斜坡	10	200~400

故障序号	NPE		SFA		TNPE		DPCA		GSFA-GNPE
故障序号	T²	SPE	S²	SPE	T²	SPE	T²	SPE	BIC-C²	BIC-SPE	BIC
1	1/0.085	1/0.005	1/0.05	1/0.005	1/0.06	0.995/0.005	0.99/0.08	1/0.05	1/0.04	1/0.04	1/0.005
2	0.945/0.1	0.95/0	0.875/0.02	0.98/0	0.97/0.15	0.965/0.125	0.915/0.25	0.95/0.135	0.925/0.03	1/0	0.995/0.03
3	0.99/0.08	1/0.01	0.98/0.01	1/0.005	1/0.065	1/0.02	0.99/0.005	1/0.03	1/0.01	1/0	1/0.01
4	0.44/0.105	0.94/0	0.925/0.065	0.475/0	0.65/0.22	0.94/0	0.93/0.07	0.915/0.05	0.815/0.04	0.91/0	0.975/0.04

基于GSFA-GNPE的动态-静态联合指标间歇过程监控

Batch Process Monitoring with Dynamic-Static Joint Indicator Based on GSFA-GNPE

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 24

相关文章 6

编辑推荐

Metrics

本文评价

[1]	郭金玉, 李文涛, 李元. 在线压缩核主元分析的自适应过程监控[J]. 上海交通大学学报, 2022, 56(10): 1397-1408.
[2]	李元, 姚宗禹. 基于邻域保持嵌入的主多项式非线性过程故障检测[J]. 上海交通大学学报, 2021, 55(8): 1001-1008.
[3]	黄健, 杨旭. 基于在线加权慢特征分析的故障检测算法[J]. 上海交通大学学报, 2020, 54(11): 1142-1150.
[4]	郭红杰1，徐春玲2，侍洪波1. 基于局部邻域标准化策略的多工况过程故障检测[J]. 上海交通大学学报（自然版）, 2015, 49(06): 868-875.
[5]	王立敏1，2，周东华1，朱城杰2. 间歇过程复合迭代学习容错保性能控制器设计[J]. 上海交通大学学报（自然版）, 2015, 49(06): 743-750.
[6]	张欢欢. 海洋石油平台钢结构焊接质量控制[J]. 海洋工程装备与技术, 2014, 1(2): 174-176.