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 11
Issue 8
IEEE/CAA Journal of Automatica Sinica
| Citation: | L. Shi, C. Tong, T. Lan, and X. Shi, “Statistical process monitoring based on ensemble structure analysis,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1889–1891, Aug. 2024. doi: 10.1109/JAS.2017.7510877
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S. Yin, S. X. Ding, X. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Trans. Ind. Electron., vol. 61, no. 11, pp. 6418–6428, Nov. 2014.
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Z. Ge, Z. Song, and F. Gao, “Review of recent research on data-based process monitoring,” Ind. Eng. Chem. Res., vol. 52, no. 10, pp. 3543–3562, Mar. 2013.
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G. Li, S. Qin, and D. Zhou, “Geometric properties of partial least squares for process monitoring,” Automatica, vol. 46, no. 1, pp. 204–210, Jan. 2010.
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M. Zhang, Z. Ge, Z. Song, and R. Fu, “Global-local structure analysis model and its application for fault detection and identification,” Ind. Eng. Chem. Res., vol. 50, no. 11, pp. 6837–6848, Jun. 2011.
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K. Hu and J. Yuan, “Multivariate statistical process control based on multiway locality preserving projections,” J. Process Control, vol. 18, no. 7–8. pp. 797–807, Aug.–Sep. 2008.
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J. Yu, “Local and global component analysis for process monitoring,” J. Process Control, vol. 22, no. 7, pp. 1358–1373, Aug. 2012.
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C. Tong and X. Yan, “Statistical process monitoring based on a multi-manifold projection algorithm,” Chemom. Intell. Lab. Syst., vol. 130, pp. 20–8, Jan. 2014.
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Q. Xu, M. Wu, E. Khoo, Z. Chen, and X. Li, “A hybrid ensemble deep learning approach for early prediction of battery remaining useful life,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 177–187, Jan. 2023. doi: 10.1109/JAS.2023.123024
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C. Tong, A. Palazoglu, and X. Yan, “Improved ICA for process monitoring based on ensemble learning and Bayesian inference,” Chemom. Intell. Lab. Syst., vol. 135, pp. 141–149, Jul. 2014.
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Z. Ge, M. Zhang, and Z. Song, “Nonlinear process monitoring based on linear subspace and Bayesian inference,” J. Process Control, vol. 20, no. 5, pp. 676–688, Jun. 2010.
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L. H. Chiang, E. L. Russell, and R. D. Braatz, Fault Detection and Diagnosis in Industrial System. London, UK: Spring-Verlag, 2001.
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| [12] |
Q. Zhu, “Latent variable regression for supervised modeling and monitoring,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 800–811, May 2020. doi: 10.1109/JAS.2020.1003153
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