基于仿射传播聚类子集主元分析的间歇过程监测方法

doi:10.11949/j.issn.0438-1157.20151454

化工学报 ›› 2016, Vol. 67 ›› Issue (5): 1989-1997.DOI: 10.11949/j.issn.0438-1157.20151454

基于仿射传播聚类子集主元分析的间歇过程监测方法

胡永兵^1,2,3,4, 高学金^1,2,3,4, 李亚芬^1,2,3,4, 齐咏生⁵, 王普^1,2,3,4

1 北京工业大学电子信息与控制工程学院, 北京 100124;
2 数字社区教育部工程研究中心, 北京 100124;
3 城市轨道交通北京实验室, 北京 100124;
4 计算智能与智能系统北京市重点实验室, 北京 100124;
5 内蒙古工业大学电力学院, 内蒙古呼和浩特 010051

收稿日期:2015-09-16 修回日期:2015-12-09 出版日期:2016-05-05 发布日期:2016-05-05
通讯作者: 高学金
基金资助:
国家自然科学基金项目(61174109,61364009)。

Subset multiway principal component analysis monitoring for batch process based on affinity propagation clustering

HU Yongbing^1,2,3,4, GAO Xuejin^1,2,3,4, LI Yafen^1,2,3,4, QI Yongsheng⁵, WANG Pu^1,2,3,4

1 College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2 Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;
3 Beijing Laboratory for Urban Mass Transit, Beijing 100124, China;
4 Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
5 School of Electric Power, Inner Mongolia University of Technology, Huhhot 010051, Inner Mongolia, China

Received:2015-09-16 Revised:2015-12-09 Online:2016-05-05 Published:2016-05-05
Supported by:
supported by the National Natural Science Foundation of China (61174109, 61364009).

摘要/Abstract

摘要：

针对间歇过程固有的多阶段特性,也为了克服传统阶段划分方法严格按照物理时刻顺序将采样点硬性分割而不能使其寻找数据特征最为相近的聚类中心的严重缺陷,提出基于仿射传播聚类(AP)的子集多向主元分析(subset-MPCA)监测新方法:采用全新的乱序聚类思想,将时间片矩阵打乱用AP进行无约束乱序聚类,使样本突破时间顺序的约束自由找寻与其特征最为相近的聚类中心,获得聚类子集,建立精确的子集MPCA监控模型。在线监控时,引入信息度传递实现实时采样点的阶段归属判断,解决阶段不等长批次的最佳模型选择问题。对青霉素仿真数据的实验表明,该方法较传统方法可有效降低故障的漏报和误报,有着更加可靠的监控性能。

关键词: 间歇过程, 子集聚类, 过程监测, 阶段归属, 子集多向主元分析

Abstract:

For the multiphase property inherent in the batch process, and in order to overcome the serious deficiency that the traditional stage partition method divided the sampling points into several categories strictly according to the sampling time sequence and cannot make it look for the clustering center with the most similar data characteristics, a novel subset-MPCA method based on affinity propagation (AP) clustering is proposed. Using a new idea of random order clustering, this method disrupts the order of the time slice and AP is used to cluster with the random order. Thus, each data point can break the restriction of the time sequence and find the clustering center which has the most similar data characteristics with it, obtaining clustering subset and establishing the precise model. For online monitoring, information transmission is introduced to determine the stage attribution of real time sampling points to solve the problem of optimal model selection for unequal length batch. Experiments on penicillin simulation data show that this method can effectively reduce the leaking alarms and nuisance alarms than the traditional method, having more reliable monitoring performance.

Key words: batch process, subset clustering, process monitoring, stage attribution, subset MPCA

中图分类号:

TP277

胡永兵, 高学金, 李亚芬, 齐咏生, 王普. 基于仿射传播聚类子集主元分析的间歇过程监测方法[J]. 化工学报, 2016, 67(5): 1989-1997.

HU Yongbing, GAO Xuejin, LI Yafen, QI Yongsheng, WANG Pu. Subset multiway principal component analysis monitoring for batch process based on affinity propagation clustering[J]. CIESC Journal, 2016, 67(5): 1989-1997.

参考文献 21

[1]	贾润达, 毛志忠, 王福利. 基于KPLS模型的间歇过程产品质量控制 [J]. 化工学报, 2013, 64 (4): 1332-1339. DOI: 10.3969/j.issn.0438-1157.2013. 04. 029. JIA R D, MAO Z Z, WANG F L. KPLS model based product quality control for batch processes [J]. CIESC Journal, 2013, 64 (4): 1332-1339. DOI: 10.3969/j.issn.0438-1157.2013.04.029.
[2]	QIN S J. Survey on data-driven industrial process monitoring and diagnosis [J]. Annual Reviews in Control, 2012, 36 (2): 220-234. DOI: 10.1016/j.arcontrol.2012.09.004.
[3]	张子羿, 胡益, 侍洪波. 一种基于聚类方法的多阶段间歇过程监控方法 [J]. 化工学报, 2013, 64 (12): 4522-4528. DOI: 10.3969/j.issn. 0438-1157.2013.12.038. ZHANG Z Y, HU Y, SHI H B. Multi-stage batch process monitoring based on a clustering method [J]. CIESC Journal, 2013, 64 (12): 4522-4528. DOI: 10.3969/j.issn.0438-1157.2013.12.038.
[4]	QIN S J. Process data analytics in the era of big data [J]. AIChE Journal, 2014, 60 (9): 3092-3100. DOI: 10.1002/aic.14523.
[5]	GE Z Q, SONG Z H, GAO F R. Review of recent research on data-based process monitoring [J]. Industrial and Engineering Chemistry Research, 2013, 52 (10): 3543-3562. DOI: 10.1021/ie302069q
[6]	NOMIKOS P, MACGREGOR J F. Monitoring batch processes using multiway principal component analysis [J]. AIChE Journal, 1994, 40 (8): 1361-1375.
[7]	常玉清, 王姝, 谭帅, 等. 基于多时段MPCA模型的间歇过程监测方法研究 [J]. 自动化学报, 2010, 36 (9): 1312-1320. DOI: 10.3724/SP.J.1004. 2010. 01312. CHANG Y Q, WANG S, TAN S, et al. Research on multistage-based MPCA modeling and monitoring method for batch processes [J]. Acta Automatica Sinica, 2010, 36 (9): 1312-1320. DOI: 10.3724/SP.J.1004. 2010.01312.
[8]	GUO H, LI H G. On-line batch process monitoring with improved multi-way independent component analysis [J]. Chinese Journal of Chemical Engineering, 2013, 21 (3): 263-270. DOI: 10.1016/S1004-9541(13)60485-4.
[9]	CAI L F, TIAN X M, CHEN S. A process monitoring method based on noisy independent component analysis [J]. Neurocomputing, 2014, 127: 231-246. DOI: 10.1016/j.neucom.2013.07.029.
[10]	NOMIKOS P, MACGREGOR J F. Multiway partial least squares in monitoring batch progresses [J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30 (1): 97-108.
[11]	GE Z Q, SONG Z H, ZHAO L P, et al. Two-level PLS model for quality prediction of multiphase batch processes [J]. Chemometrics and Intelligent Laboratory Systems, 2014, 130: 29-36. DOI: 10.1016/j.chemolab. 2013. 09. 008.
[12]	齐咏生, 王普, 高学金, 等. 一种新的多阶段间歇过程在线监控策略 [J]. 仪器仪表学报, 2011, 32 (6): 1290-1297. QI Y S, WANG P, GAO X J, et al. Novel online monitoring strategy for multiphase batch processes [J]. Chinese Journal of Scientific Instrument, 2011, 32 (6): 1290-1297.
[13]	WOLD S, KETTANEH N, TJESSEM K. Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection [J]. Journal of Chemometrics, 1996, 10 (5/6): 463-482. DOI: 10.1002/(SICI)1099-128X(199609)10:5/63.0.CO.
[14]	LU N Y, GAO F R, WANG F L. Sub-PCA modeling and on-line monitoring strategy for batch processes [J]. AIChE Journal, 2004, 50 (1): 255-259. DOI: 10.1002/aic.10024.
[15]	ZHAO C H, WANG F L, LU N Y, et al. Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes [J]. Journal of Process Control, 2007, 17 (9): 728-741. DOI: 10. 1016/j.jprocont.2007.02.005.
[16]	FREY B J, DUECK D. Clustering by passing messages between data points [J]. Science, 2007, 315 (5814): 972-976.
[17]	RANNAR S, MACGREGOR J F, WOLD S. Adaptive batch monitoring using hierarchical PCA [J]. Chemometrics and Intelligent Laboratory Systems, 1998, 41 (1): 73-81. DOI: 10. 1016/S0169-7439(98)00024-0.
[18]	BIROL G, ÜNDEY C, INAR A. A modular simulation package for fed-batch fermentation: penicillin production [J]. Computers & Chemical Engineering, 2002, 26 (11): 1553-1565. DOI: 10.1016/S0098-1354(02)00127-8.
[19]	常鹏, 王普, 高学金, 等. 基于核熵投影技术的多阶段间歇过程监测研究 [J]. 仪器仪表学报, 2014, 35 (7): 1654-1661. CHANG P, WANG P, GAO X J, et al. Research on batch process monitoring based on multi-stage kernel pattern entropy projection technology [J]. Chinese Journal of Scientific Instrument, 2014, 35 (7): 1654-1661.
[20]	STUBBS S, ZHANG J, MORRIS J. Multiway interval partial least squares for batch process performance monitoring [J]. Industrial & Engineering Chemistry Research, 2013, 52 (35): 12399-12407. DOI: 10.1021/ie303562t.
[21]	VANLAER J, GINS G, VAN IMPE J F M. Quality assessment of a variance estimator for Partial Least Squares prediction of batch-end quality [J]. Computers & Chemical Engineering, 2013, 52: 230-239. DOI: 10.1016/j.compchemeng.2013.01.012.

基于仿射传播聚类子集主元分析的间歇过程监测方法

Subset multiway principal component analysis monitoring for batch process based on affinity propagation clustering

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价

[1]	宋冰, 郑城风, 侍洪波, 陶阳, 谭帅. 基于VAE-OCCA的质量相关故障检测方法研究[J]. 化工学报, 2023, 74(4): 1630-1638.
[2]	王雅琳, 潘雨晴, 刘晨亮. 基于GSA-LSTM动态结构特征提取的间歇过程监测方法[J]. 化工学报, 2022, 73(9): 3994-4002.
[3]	朱雄卓, 张瀚文, 杨春节. 基于高斯混合模型的MWPCA高炉异常监测算法[J]. 化工学报, 2021, 72(3): 1539-1548.
[4]	高学金, 刘腾飞, 徐子东, 高慧慧, 于涌川. 基于循环自动编码器的间歇过程故障监测[J]. 化工学报, 2020, 71(7): 3172-3179.
[5]	郑必鸣, 史彬, 鄢烈祥. 多因素不确定条件下的间歇生产调度优化[J]. 化工学报, 2020, 71(3): 1246-1253.
[6]	于蕾, 邓晓刚, 曹玉苹, 路凯琪. 基于变量分组DTW-MCVA的不等长间歇过程故障检测方法[J]. 化工学报, 2019, 70(9): 3441-3448.
[7]	王立敏, 卢丽彬, 高福荣, 周东华. 多阶段间歇过程无穷时域优化线性二次容错控制[J]. 化工学报, 2019, 70(2): 541-547.
[8]	郭孝正, 刘琳琳, 张磊, 都健. 基于截断器半连续操作的间歇过程性质集成[J]. 化工学报, 2019, 70(2): 516-524.
[9]	高学金, 黄梦丹, 齐咏生, 王普. PDPSO优化多阶段AR-PCA间歇过程监测方法[J]. 化工学报, 2018, 69(9): 3914-3923.
[10]	张雷, 张小刚, 陈华. 基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量[J]. 化工学报, 2018, 69(6): 2576-2585.
[11]	常鹏, 乔俊飞, 王普, 高学金, 李征. 基于MKECA的非高斯性和非线性共存的间歇过程监测[J]. 化工学报, 2018, 69(3): 1200-1206.
[12]	刘晓凤, 栾小丽, 刘飞. 基于隐变量空间载荷余弦相似度的间歇过程递推优化[J]. 化工学报, 2018, 69(3): 1167-1172.
[13]	王建林, 马琳钰, 刘伟旻, 邱科鹏, 于涛. 基于核相似度支持向量数据描述的间歇过程监测[J]. 化工学报, 2017, 68(9): 3494-3500.
[14]	刘伟旻, 王建林, 邱科鹏, 于涛, 赵利强. 基于多证据融合决策的间歇过程测量数据异常检测方法[J]. 化工学报, 2017, 68(8): 3183-3189.
[15]	王建林, 刘伟旻, 邱科鹏, 张维佳, 于涛. 基于LWPT-DTW的间歇过程不等长时段数据同步化[J]. 化工学报, 2017, 68(7): 2866-2872.