System Development & Application
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
807-813

Automatic fault diagnosis method based on neural architecture search for industrial processes

Li Xian1,2,3,4
Li Xin1,2,3
Zhou Xiaofeng1,2,3
Li Shuai1,2,3,4
Jin Liang1,2,3
1. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, 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.1001-3695.2021.08.0362

Abstract

In the industrial process, the existing fault diagnosis methods based on deep neural network are a challenging pro-blem due to complicated network structure design and time-consuming parameter optimization. To achieve the problem, this paper proposed an automatic fault diagnosis method(AutoFD) based on neural architecture search. The method used the AutoFD neural architecture search(NAS) algorithm to automatically complete the network structure design of the convolutional neural network and the optimization of the network parameters. On this basis, some new channels were firstly generated by operating the original data. Performance prediction was then used to speed up the process of acquiring channel adaptive sorting, and the optimal number of convolutional channels was quickly selected according to the adaptive sorting of the channels. Finally, through the optimal convolution channels, the best-performing multi-channel convolutional neural network model for automatic fault diagnosis in chemical processes could be searched. It was applied to the Tennessee Eastman(TE) industrial process and numerical system for fault diagnosis to verify the proposed method. The results show that the AutoFD method can automatically design the network structure and optimize the parameters of the multi-channel convolutional neural network, which has excellent performance in fault diagnosis.

Foundation Support

辽宁省“兴辽英才计划”资助项目(XLYC1808009)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.08.0362
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: System Development & Application
Pages: 807-813
Serial Number: 1001-3695(2022)03-028-0807-07

Publish History

[2021-11-29] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

李显, 李歆, 周晓锋, 等. 基于网络结构搜索的工业过程自动故障诊断方法 [J]. 计算机应用研究, 2022, 39 (3): 807-813. (Li Xian, Li Xin, Zhou Xiaofeng, et al. Automatic fault diagnosis method based on neural architecture search for industrial processes [J]. Application Research of Computers, 2022, 39 (3): 807-813. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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