A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 13
Issue 1
IEEE/CAA Journal of Automatica Sinica
| Citation: | L. Cao, J. Su, F. Yang, Y. Cao, and B. Gopaluni, “Interpretable and reliable soft sensor development in Industry 5.0,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 1, pp. 236–238, Jan. 2026. doi: 10.1109/JAS.2025.125420
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F. Tao, H. Zhang, A. Liu and A. Y. C. Nee, “Digital twin in industry: State-of-the-art,” IEEE Trans. Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, 2019. doi: 10.1109/TII.2018.2873186
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R. B. Gopaluni, A. Tulsyan, B. Chachuat, et al., “Modern machine learning tools for monitoring and control of industrial processes: A survey,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 218–229, 2020. doi: 10.1016/j.ifacol.2020.12.126
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P. Kadlec, B. Gabrys, and S. Strandt, “Data-driven soft sensors in the process industry,” Computers & Chemical Engineering, vol. 33, no. 4, pp. 795–814, 2009. doi: 10.1016/j.compchemeng.2008.12.012
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Z. Lou, Y. Wang, Y. Si, and S. Lu, “A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysis,” Automatica, vol. 138, p. 110148, 2022. doi: 10.1016/j.automatica.2021.110148
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W. Xiang, K. Yu, F. Han, L. Fang, D. He, and Q.-L. Han, “Advanced manufacturing in Industry 5.0: A survey of key enabling technologies and future trends,” IEEE Trans. Industrial Informatics, vol. 20, no. 2, pp. 1055–1068, 2023.
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European Commission: Directorate-General for Research and Innovation, “ERA industrial technologies roadmap on human-centric research and innovation for the manufacturing sector”, Luxembourg City, Luxembourg: Publications Office of the European Union, 2024.
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L. Auret and C. Aldrich, “Interpretation of nonlinear relationships in process data using random forests,” J. Process Control, vol. 22, no. 3, pp. 490–503, 2012.
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Q. Deng, Z. Chen, W. Zhu, Z. Li, Y. Yuan, and Y. Wang, “Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data,” Energy and Buildings, vol. 324, p. 114901, 2024. doi: 10.1016/j.enbuild.2024.114901
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B. Sun, C. Yang, Y. Wang, W. Gui, I. Craig, and L. Olivier, “A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes,” J. Process Control, vol. 86, pp. 30–43, 2020. doi: 10.1016/j.jprocont.2019.11.012
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X. Zhang, C. Song, B. Huang, and J. Zhao, “Bayesian-based causal structure inference with a domain knowledge prior for stable and interpretable soft sensing,” IEEE Trans. Cybern., vol. 54, pp. 6081–6094, 2024. doi: 10.1109/TCYB.2024.3431636
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X. Gao, Y. Huang, and Y. A. W. Shardt, “Discovering latent causal variables using a trade-off between compression and causality,” IFAC-PapersOnLine, vol. 58, no. 14, pp. 1–6, 2024. doi: 10.1016/j.ifacol.2024.08.304
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F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv preprint arXiv: 1702.08608, 2017.
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M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you? Explaining the predictions of any classifier,” in Proc. 22nd ACM SIGKDD International Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
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T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial Intelligence, vol. 267, pp. 1–38, 2019. doi: 10.1016/j.artint.2018.07.007
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