Adaptive partitioning PCA model for improving fault detection and isolation

Kangling Liu; Xin Jin; Zhengshun Fei; Jun Liang

doi:10.1016/j.cjche.2014.09.052

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Adaptive partitioning PCA model for improving fault detection and isolation

过程系统工程与过程安全 | Updated：2026-01-06

- Adaptive partitioning PCA model for improving fault detection and isolation
- Adaptive partitioning PCA model for improving fault detection and isolation
- 中国化学工程学报（英文版） 2015年第6期页码：981-991
- Affiliations：
  
  1. State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, Zhejiang University,Hangzhou,China,310027
  2. 2 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology,Hangzhou,China,310023
- Author bio：
- Funds：
  
  Support by theNational Natural Science Foundation of China (61174114), the Research Fund for the Doctoral Program of Higher Education in China (20120101130016) and Zhejiang Provincial Science and Technology Planning Projects of China (2014C31019).
- DOI：10.1016/j.cjche.2014.09.052
  中图分类号：
- 纸质出版：2015
- Accepted：
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Kangling Liu, Xin Jin, Zhengshun Fei, 等. Adaptive partitioning PCA model for improving fault detection and isolation[J]. 中国化学工程学报（英文版）, 2015,(6):981-991.

Kangling Liu, Xin Jin, Zhengshun Fei, et al. Adaptive partitioning PCA model for improving fault detection and isolation[J]. Chinese Journal of Chemical Engineering, 2015, (6): 981-991.
Kangling Liu, Xin Jin, Zhengshun Fei, 等. Adaptive partitioning PCA model for improving fault detection and isolation[J]. 中国化学工程学报（英文版）, 2015,(6):981-991. DOI： 10.1016/j.cjche.2014.09.052.

Kangling Liu, Xin Jin, Zhengshun Fei, et al. Adaptive partitioning PCA model for improving fault detection and isolation[J]. Chinese Journal of Chemical Engineering, 2015, (6): 981-991. DOI： 10.1016/j.cjche.2014.09.052.

摘要

Abstract

In chemical process

a large number of measured andmanipulated variables are highly correlated. Principal component analysis (PCA) iswidely 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 effect. The 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.

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