Causal temporal graph attention network for fault diagnosis of chemical processes

Jiaojiao Luo; Zhehao Jin; Heping Jin; Qian Li; Xu Ji; Yiyang Dai

doi:10.1016/j.cjche.2024.01.019

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Causal temporal graph attention network for fault diagnosis of chemical processes

Updated：2026-01-06

- Causal temporal graph attention network for fault diagnosis of chemical processes
- Causal temporal graph attention network for fault diagnosis of chemical processes
- 中国化学工程学报（英文版） 2024年70卷第6期页码：20-32
- Affiliations：
  
  1. School of Chemical Engineering, Sichuan University,Chengdu,China,610065
  2. China Three Gorges Corporation,Beijing,China,100038
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2024.01.019
  中图分类号：
- 纸质出版：2024
- Accepted：
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Jiaojiao Luo, Zhehao Jin, Heping Jin, 等. Causal temporal graph attention network for fault diagnosis of chemical processes[J]. 中国化学工程学报（英文版）, 2024,70(6):20-32.

Jiaojiao Luo, Zhehao Jin, Heping Jin, Qian Li, Xu Ji, Yiyang Dai. Causal temporal graph attention network for fault diagnosis of chemical processes[J]. Chinese Journal of Chemical Engineering, 2024, 70(6): 20-32.
Jiaojiao Luo, Zhehao Jin, Heping Jin, 等. Causal temporal graph attention network for fault diagnosis of chemical processes[J]. 中国化学工程学报（英文版）, 2024,70(6):20-32. DOI： 10.1016/j.cjche.2024.01.019.

Jiaojiao Luo, Zhehao Jin, Heping Jin, Qian Li, Xu Ji, Yiyang Dai. Causal temporal graph attention network for fault diagnosis of chemical processes[J]. Chinese Journal of Chemical Engineering, 2024, 70(6): 20-32. DOI： 10.1016/j.cjche.2024.01.019.

摘要

Abstract

Fault detection and diagnosis (FDD) plays a significant role in ensuring the safety and stability of chemical processes. With the development of artificial intelligence (AI) and big data technologies

data-driven approaches with excellent performance are widely used for FDD in chemical processes. However

improved predictive accuracy has often been achieved through increased model complexity

which turns models into black-box methods and causes uncertainty regarding their decisions. In this study

a causal temporal graph attention network (CTGAN) is proposed for fault diagnosis of chemical processes. A chemical causal graph is built by causal inference to represent the propagation path of faults. The attention mechanism and chemical causal graph were combined to help us notice the key variables relating to fault fluctuations. Experiments in the Tennessee Eastman (TE) process and the green ammonia (GA) process showed that CTGAN achieved high performance and good explainability.

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