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

doi:10.11949/j.issn.0438-1157.20151454

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

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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).

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

通讯作者: 高学金
基金资助:
国家自然科学基金项目(61174109,61364009)。

Abstract

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

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

CLC Number:

TP277

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.

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.

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

基于仿射传播聚类子集主元分析的间歇过程监测方法

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