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A Novel Deep Learning Representation for Industrial Control System Data

Bowen Zhang1,2,3, Yanbo Shi4, Jianming Zhao1,2,3,*, Tianyu Wang1,2,3, Kaidi Wang5

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 and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China
4 Shenyang Aircraft Corporation, Shenyang, 110850, China
5 Molarray Research, Toronto, L4B3K1, Canada

* Corresponding Author: Jianming Zhao. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 2703-2717. https://doi.org/10.32604/iasc.2023.033762

Abstract

Feature extraction plays an important role in constructing artificial intelligence (AI) models of industrial control systems (ICSs). Three challenges in this field are learning effective representation from high-dimensional features, data heterogeneity, and data noise due to the diversity of data dimensions, formats and noise of sensors, controllers and actuators. Hence, a novel unsupervised learning autoencoder model is proposed for ICS data in this paper. Although traditional methods only capture the linear correlations of ICS features, our deep industrial representation learning model (DIRL) based on a convolutional neural network can mine high-order features, thus solving the problem of high-dimensional and heterogeneous ICS data. In addition, an unsupervised denoising autoencoder is introduced for noisy ICS data in DIRL. Training the denoising autoencoder allows the model to better mitigate the sensor noise problem. In this way, the representative features learned by DIRL could help to evaluate the safety state of ICSs more effectively. We tested our model with absolute and relative accuracy experiments on two large-scale ICS datasets. Compared with other popular methods, DIRL showed advantages in four common indicators of AI algorithms: accuracy, precision, recall, and F1-score. This study contributes to the effective analysis of large-scale ICS data, which promotes the stable operation of ICSs.

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APA Style
Zhang, B., Shi, Y., Zhao, J., Wang, T., Wang, K. (2023). A novel deep learning representation for industrial control system data. Intelligent Automation & Soft Computing, 36(3), 2703-2717. https://doi.org/10.32604/iasc.2023.033762
Vancouver Style
Zhang B, Shi Y, Zhao J, Wang T, Wang K. A novel deep learning representation for industrial control system data. Intell Automat Soft Comput . 2023;36(3):2703-2717 https://doi.org/10.32604/iasc.2023.033762
IEEE Style
B. Zhang, Y. Shi, J. Zhao, T. Wang, and K. Wang "A Novel Deep Learning Representation for Industrial Control System Data," Intell. Automat. Soft Comput. , vol. 36, no. 3, pp. 2703-2717. 2023. https://doi.org/10.32604/iasc.2023.033762



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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