IEEE/CAA Journal of Automatica Sinica
Citation: | Qinqin 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 |
[1] |
J. F. MacGregor, C. Jaeckle, C. Kiparissides, and M. Koutoudi, “Process monitoring and diagnosis by multiblock PLS methods,” AIChE Journal, vol. 40, no. 5, pp. 826–838, 1994. doi: 10.1002/aic.690400509
|
[2] |
S. Joe Qin, “Statistical process monitoring: basics and beyond,” J. Chemometrics, vol. 17, no. 8–9, pp. 480–502, 2003. doi: 10.1002/cem.800
|
[3] |
X. Zhang, W. W. Yan, X. Zhao, and H. H. Shao, “Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis,” Process Biochemistry, vol. 42, no. 8, pp. 1200–1210, Aug. 2007. doi: 10.1016/j.procbio.2007.05.016
|
[4] |
A. P. Ferreira and M. Tobyn, “Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era,” Pharmaceutical Development and Technology, vol. 20, no. 5, pp. 513–527, 2015. doi: 10.3109/10837450.2014.898656
|
[5] |
X. Luo, M. C. Zhou, Y. N. Xia, Q. S. Zhu, A. C. Ammari, and A. Alabdulwahab, “Generating highly accurate predictions for missing QoS data via aggregating nonnegative latent factor models,” IEEE Trans. Neural Networks and Learning Systems, vol. 27, no. 3, pp. 524–537, Apr. 2015.
|
[6] |
X. Luo, M. C. Zhou, S. Li, Y. N. Xia, Z.-H. You, Q. S. Zhu, and H. Leung, “Incorporation of efficient second-order solvers into latent factor models for accurate prediction of missing QoS data,” IEEE Trans. Cybernetics, vol. 48, no. 4, pp. 1216–1228, Apr. 2017.
|
[7] |
J. E. Jackson, A User’s Guide to Principal Components. John Wiley & Sons, 2005, vol. 587.
|
[8] |
S. J. Qin, “Survey on data-driven industrial process monitoring and diagnosis,” Annual Reviews in Control, vol. 36, no. 2, pp. 220–234, 2012. doi: 10.1016/j.arcontrol.2012.09.004
|
[9] |
D. H. Zhou, G. Li, and S. J. Qin, “Total projection to latent structures for process monitoring,” AIChE Journal, vol. 56, no. 1, pp. 168–178, Jan. 2019.
|
[10] |
S. J. Qin and Y. Y. Zheng, “Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures,” AIChE Journal, vol. 59, no. 2, pp. 496–504, Feb. 2013. doi: 10.1002/aic.13959
|
[11] |
L. Sun, S. W. Ji, S. P. Yu, and J. P. Ye, “On the equivalence between canonical correlation analysis and orthonormalized partial least squares,” in Proc. 21st Int. Joint Conf. Artificial Intelligence, vol. 9, pp. 1230–1235, 2009.
|
[12] |
Q. Q. Zhu, Q. Liu, and S. J. Qin, “Concurrent quality and process monitoring with canonical correlation analysis,” J. Process Control, vol. 60, pp. 95–103, Dec. 2017. doi: 10.1016/j.jprocont.2017.06.017
|
[13] |
G. Li, S. J. Qin, and D. H. Zhou, “Geometric properties of partial least squares for process monitoring,” Automatica, vol. 46, no. 1, pp. 204–210, Jan. 2010. doi: 10.1016/j.automatica.2009.10.030
|
[14] |
Z. W. Chen, S. X. Ding, K. Zhang, Z. B. Li, and Z. K. Hu, “Canonical correlation analysis-based fault detection methods with application to alumina evaporation process,” Control Engineering Practice, vol. 46, pp. 51–58, Jan. 2016. doi: 10.1016/j.conengprac.2015.10.006
|
[15] |
Q. Zhu and S. J. Qin, “Supervised diagnosis of quality and process faults with canonical correlation analysis,” Industrial & Engineering Chemistry Research, 2019.
|
[16] |
Q. C. Jiang, X. F. Yan, and B. Huang, “Neighborhood variational bayesian multivariate analysis for distributed process monitoring with missing data,” IEEE Trans. Control Systems Technology, vol. 27, no. 6, pp. 2330–2339, Oct. 2018.
|
[17] |
Q. C. Jiang, X. F. Yan, and B. Huang, “Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes,” Industrial &Engineering Chemistry Research, vol. 58, no. 29, pp. 12899–12912, 2019.
|
[18] |
Z. Q. Ge, “Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes,” J. Process Control, vol. 65, pp. 107–117, May 2018. doi: 10.1016/j.jprocont.2017.08.010
|
[19] |
P. Geladi and B. R. Kowalski, “Partial least-squares regression: a tutorial,” Analytica Chimica Acta, vol. 185, pp. 1–17, 1986. doi: 10.1016/0003-2670(86)80028-9
|
[20] |
A. Höskuldsson, “PLS regression methods,” J. Chemometrics, vol. 2, no. 3, pp. 211–228, 1988. doi: 10.1002/cem.1180020306
|
[21] |
H. Hotelling, “Relations between two sets of variates,” Biometrika, vol. 28, no. 3/4, pp. 321–377, 1936. doi: 10.2307/2333955
|
[22] |
Q. Q. Zhu and S. J. Qin, “Latent variable regression for process and quality modeling, ” in 1st Int. Conf. on Industrial Artificial Intelligence (IAI). IEEE, 2019, pp. 1–6.
|
[23] |
Q. Q. Zhu, S. J. Qin, and Y. Dong, “Dynamic latent variable regression for inferential sensor modeling and supervised monitoring,” Submitted to Computers &Chemical Engineering, 2019.
|
[24] |
Q. Q. Zhu, “Dynamic latent variable regression for inferential sensor modeling and supervised monitoring,” Submitted to 21st IFAC World Congress, 2019.
|
[25] |
R. Rosipal and N. Krämer, “Overview and recent advances in partial least squares,” in Proc. Int. Statistical and Optimization Perspectives Workshop “Subspace, Latent Structure and Feature Selection,” Berlin, Germany: Springer, 2005, pp. 34–51.
|
[26] |
J. J. Downs and E. F. Vogel, “A plant-wide industrial process control problem,” Computers &Chemical Engineering, vol. 17, no. 3, pp. 245–255, 1993.
|