基于奇异值识别的模式切换过程递归PCA监控  

A process singular value recognition based recursive PCA approach to mode-transition process monitoring

作　　者：王倩[1] 李宏光[1] 

机构地区：[1]北京化工大学信息科学与技术学院,北京10029

出　　处：《化工学报》2012年第9期2948-2952,共5页CIESC Journal

摘　　要：常规的PCA方法难于对发生模式变化的过程参数进行监控,为此,本文提出了一种基于奇异值识别递归PCA技术,用于解决多模式切换过程的监控问题。首先建立了在线奇异值识别算法,通过识别奇异值的变化可以准确判断过程发生模式切换的时间,然后采用递归PCA对过程的模式切换过渡阶段进行监控。将TE过程用于实例研究,验证了所提出方法的有效性。Conventional PCA method is susceptible to intractable difficulties to monitor a process experiencing mode transitions.In this context,this paper presents a modified recursive PCA approach which combines on-line process singular value recognition algorithms and contributes to mode transition process monitoring.Specifically,an enabling algorithm of process singular value recognition is proposed,which accounts for accurately identifying ongoing mode transition of a process before launching recursive PCA algorithms to monitor the transition phases.TE process is employed as a case study,which demonstrates the benefits of the contribution.

关 键 词：多模式过程 监控 奇异值识别 递归PCA 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]