Fault diagnosis for distillation process based on CNN–DAE

doi:10.1016/j.cjche.2018.12.021

中国化学工程学报 ›› 2019, Vol. 27 ›› Issue (3): 598-604.DOI: 10.1016/j.cjche.2018.12.021

• Process Systems Engineering and Process Safety • 上一篇下一篇

Fault diagnosis for distillation process based on CNN–DAE

Chuankun Li^1,2, Dongfeng Zhao³, Shanjun Mu², Weihua Zhang², Ning Shi², Lening Li²

1 College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China;
2 State Key Laboratory of Safety and Control for Chemicals, SINOPEC Qingdao Research Institute of Safety Engineering, Qingdao 266071, China;
3 College of Chemical Engineering, China University of Petroleum, Qingdao 266580, China

收稿日期:2018-09-14 修回日期:2018-12-21 出版日期:2019-03-28 发布日期:2019-04-25
通讯作者: Dongfeng Zhao,E-mail address:zhaodf@vip.sina.com
基金资助:
Supported by the National Natural Science Foundation of China (21706291,61751305).

Fault diagnosis for distillation process based on CNN–DAE

Chuankun Li^1,2, Dongfeng Zhao³, Shanjun Mu², Weihua Zhang², Ning Shi², Lening Li²

1 College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, China;
2 State Key Laboratory of Safety and Control for Chemicals, SINOPEC Qingdao Research Institute of Safety Engineering, Qingdao 266071, China;
3 College of Chemical Engineering, China University of Petroleum, Qingdao 266580, China

Received:2018-09-14 Revised:2018-12-21 Online:2019-03-28 Published:2019-04-25
Contact: Dongfeng Zhao,E-mail address:zhaodf@vip.sina.com
Supported by:
Supported by the National Natural Science Foundation of China (21706291,61751305).

摘要/Abstract

摘要： Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders (DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks (CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN-DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.

关键词: Convolutional neural networks, Deep auto-encoders, Distillation process, Fault diagnosis

Abstract: Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders (DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks (CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN-DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.

Key words: Convolutional neural networks, Deep auto-encoders, Distillation process, Fault diagnosis

Chuankun Li, Dongfeng Zhao, Shanjun Mu, Weihua Zhang, Ning Shi, Lening Li. Fault diagnosis for distillation process based on CNN–DAE[J]. 中国化学工程学报, 2019, 27(3): 598-604.

Chuankun Li, Dongfeng Zhao, Shanjun Mu, Weihua Zhang, Ning Shi, Lening Li. Fault diagnosis for distillation process based on CNN–DAE[J]. Chinese Journal of Chemical Engineering, 2019, 27(3): 598-604.

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Fault diagnosis for distillation process based on CNN–DAE

Fault diagnosis for distillation process based on CNN–DAE

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