Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

Shanwei Xiong; Li Zhou; Yiyang Dai; Xu Ji

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Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

Full Length Article | Updated：2026-01-06

- Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis
- Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis
- 中国化学工程学报（英文版） 2023年56卷第4期页码：1-14
- Affiliations：
  
  School of Chemical Engineering, Sichuan University,Chengdu,China,610065
- Author bio：
- Funds：
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  中图分类号：
- 纸质出版：2023
- Accepted：
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Shanwei Xiong, Li Zhou, Yiyang Dai, 等. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. 中国化学工程学报（英文版）, 2023,56(4):1-14.

Shanwei Xiong, Li Zhou, Yiyang Dai, Xu Ji. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 1-14.
Shanwei Xiong, Li Zhou, Yiyang Dai, 等. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. 中国化学工程学报（英文版）, 2023,56(4):1-14. DOI：

Shanwei Xiong, Li Zhou, Yiyang Dai, Xu Ji. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 1-14. DOI：

摘要

Abstract

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology

data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years

methods based on deep neural networks have made significant breakthroughs

and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore

we propose a fusion deep-learning algorithm based on a fully convolutional neural network (FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory (LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model

aimed at highlighting the importance of features

which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process

our proposed model exhibits impressive performance

demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

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