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A Model Transfer Learning Based Fault Diagnosis Method for Chemical Processes With Small Samples

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  • Intelligent Control and Applications
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Abstract

Traditional fault diagnosis methods relies on sufficient fault samples, but it is unrealistic since the fault is a low possibility event in real industrial scenes. To address the above issue, this paper proposed a fault diagnosis method for chemical processes with small samples. First, a data self-generating-based transfer learning (DSGTL) method is presented to expand the fault samples. The characteristic of fault data is learned by adversarial relation and transferred to the generated data. Moreover, a model-based transfer learning strategy is adopted to improve the robustness of the proposed method to the quality of generated data. Second, the sample reconstruction-based convolutional neural network (SR-CNN) is proposed which adaptively extracts features from both spatial domain and time domain and identifies the fault type of industrial process with small samples. Finally, the experimental result of the Tennessee Eastman (TE) process proves the validity and the feasibility of the proposed method.

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Authors and Affiliations

  1. Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Jun-Wei Zhu & Bo Wang

  2. School of Mathematical Science, Heilongjiang University, Harbin, 150080, China

    Xin Wang

  3. Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Heilongjiang University, Harbin, 150080, China

    Xin Wang

Authors
  1. Jun-Wei Zhu
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  2. Bo Wang
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Correspondence to Jun-Wei Zhu.

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The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

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This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LZ21F030004, in part by the Key Research and Development Program of Zhejiang under Grant 2022C01018, and in part by the National Natural Science Foundation of China under Grant U21B2001.

Jun-Wei Zhu received his B.S. degree in control theory and engineering from Northeastern University, China, in 2008, an M.S. degree in control theory and engineering from Shenyang University, China, in 2011, and a Ph.D. degree in control theory and engineering from Northeastern University, China, in 2016. He is currently a special-termed Associate Professor at the College of Information Engineering, Zhejiang University of Technology. He is also a visiting professor of the Institute for Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany, from September 2019 to September 2020. His research interests include cyber-physical systems, fault diagnosis, and fault tolerant control.

Bo Wang received her B.S. degree in automation from the Anhui Normal University, China, in 2020. She is currently working toward a master’s degree in control science and engineering from the Zhejiang University of Technology, Hangzhou, China. Her research interests include Fault classification, fault diagnosis, and fault-tolerant control.

Xin Wang received his B.S. degree in information and computing science and an M.S. degree in operational research and cybernetics from Heilongjiang University, Harbin, China, in 2008 and 2011, respectively, and a Ph.D. degree in navigation guidance and control from Northeastern University, Shenyang, China, in 2016. He is currently a Lecturer with the School of Mathematical Science, Heilongjiang University, Harbin, China, and also a Post-Doctoral Fellow with the Department of Electrical Engineering, Yeungnam University, Gyeongsan, Korea. From November 2017 to November 2018, he was a Visiting Professor in the Department of Mechanical Engineering, University of Victoria, Victoria, British Columbia, Canada. His research interests include fault diagnosis, fault-tolerant control, multiagent coordination, and time-delay systems.

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Zhu, JW., Wang, B. & Wang, X. A Model Transfer Learning Based Fault Diagnosis Method for Chemical Processes With Small Samples. Int. J. Control Autom. Syst. 21, 4080–4087 (2023). https://doi.org/10.1007/s12555-022-0798-9

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  • DOI: https://doi.org/10.1007/s12555-022-0798-9

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