Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 1
84-89

Semi-supervised fault diagnosis method for chemical process based on TE-DS

Liu Jiaren1,2,3,4
Song Hong1,2,3
Li Shuai1,2,3,4
Zhou Xiaofeng1,2,3
Liu Shurui1,2,3
1. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
3. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2021.06.0229

Abstract

Aiming at the limitation that the existing chemical process fault diagnosis methods based on deep learning usually need complete labeled data to build a fault diagnosis model, this paper proposed a semi-supervised fault diagnosis method for chemical process based on temporal ensembling-dual student model. Firstly, based on the dual student model, the method guided the mutual training through the classification constraint, the stability constraint and the consistency constraint, which effectively alleviated the error accumulation. Then it used temporal ensembling to integrate the prediction of multiple previous network evaluations as consistent regularization objects to alleviate the prediction noise and reduce the training time of the model, so as to improve the classification performance and realize fault diagnosis. Finally, this paper verified the validity and feasibility of the proposed method by the Tennessee-Eastman chemical process benchmark data. Compared with supervised methods such as BNLSTM, DCNN and MCLSTM, it proves that TE-DS algorithm is superior to fault diagnosis.

Foundation Support

辽宁省自然科学基金项目(2019-MS-344)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.06.0229
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 1
Section: Algorithm Research & Explore
Pages: 84-89
Serial Number: 1001-3695(2022)01-015-0084-06

Publish History

[2021-08-31] Accepted Paper
[2022-01-05] Printed Article

Cite This Article

刘嘉仁, 宋宏, 李帅, 等. 基于TE-DS的半监督化工过程故障诊断方法 [J]. 计算机应用研究, 2022, 39 (1): 84-89. (Liu Jiaren, Song Hong, Li Shuai, et al. Semi-supervised fault diagnosis method for chemical process based on TE-DS [J]. Application Research of Computers, 2022, 39 (1): 84-89. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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