Adaptive partitioning PCA model for improving fault detection and isolation

作     者：刘康玲 金鑫 费正顺 梁军 

作者机构：State Key Lab of Industrial Control Technology Institute of Industrial Control Technology Zhejiang University School of Automation and Electrical Engineering Zhejiang University of Science and Technology 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2015年第23卷第6期

页      面：981-991页

核心收录：

学科分类：080706[工学-化工过程机械] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：Support by the National Natural Science Foundation of China(61174114) the Research Fund for the Doctoral Program of Higher Education in China(20120101130016) Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019) 

主　　题：Adaptive partitioning Fault detection Fault isolation Principal component analysis 

摘      要：In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing *** method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.