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

doi:10.16183/j.cnki.jsjtu.2020.290

Abstract

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

CLC Number:

TP277

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.

Figures/Tables 10

Fig.1

Fig.2

Tab.1

Max values in cross-correlation matrix

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

Tab.1

Tab.2

Fig.3

Tab.3

Tab.4

Tab.5

Fig.4

Fig.5

References 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

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