Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes

Jiaxin Zhang; Wenjia Luo; Yiyang Dai; Yuman Yao

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Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes

Full Length Article | Updated：2026-01-06

- Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes
- Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes
- 中国化学工程学报（英文版） 2022年47卷第7期页码：54-70
- Affiliations：
  
  1. School of Chemistry and Chemical Engineering, Southwest Petroleum University,Chengdu,China,610500
  2. School of Chemical Engineering, Sichuan University,Chengdu,China,610065
- Author bio：
- Funds：
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- 纸质出版：2022
- Accepted：
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Jiaxin Zhang, Wenjia Luo, Yiyang Dai, 等. Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes[J]. 中国化学工程学报（英文版）, 2022,47(7):54-70.

Jiaxin Zhang, Wenjia Luo, Yiyang Dai, Yuman Yao. Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes[J]. Chinese Journal of Chemical Engineering, 2022, 47(7): 54-70.
Jiaxin Zhang, Wenjia Luo, Yiyang Dai, 等. Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes[J]. 中国化学工程学报（英文版）, 2022,47(7):54-70. DOI：

Jiaxin Zhang, Wenjia Luo, Yiyang Dai, Yuman Yao. Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes[J]. Chinese Journal of Chemical Engineering, 2022, 47(7): 54-70. DOI：

摘要

Abstract

Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis. In this article

(I) the cycle temporal algorithm (CTA) combined with the dynamic kernel principal component analysis (DKPCA) and the multiway dynamic kernel principal component analysis (MDKPCA) fault detection algorithms are proposed

which are used for continuous and batch process fault detections

respectively. In addition

(II) a fault variable identification model based on reconstructed-based contribution (RBC) model that paves the way for determining the cause of the fault are proposed. The proposed fault diagnosis model was applied to Tennessee Eastman (TE) process and penicillin fermentation process for fault diagnosis. And compare with other fault diagnosis methods. The results show that the proposed method has better detection effects than other methods. Finally

the reconstruction-based contribution (RBC) model method is used to accurately locate the root cause of the fault and determine the fault path.

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