[1] Tran, P. H., Ahmadi Nadi, A., Nguyen, T. H., Tran, K. D., & Tran, K. P. (2022). Application of machine learning in statistical process control charts: A survey and perspective. In Control charts and machine learning for anomaly detection in manufacturing (pp. 7–42). Springer. https://doi.org/10.1007/978-3-030-83819-5_2
[2] Ramos, M., Ascencio, J., Hinojosa, M. V., Vera, F., Ruiz, O., Jimenez-Feijoó, M. I., & Galindo, P. (2021). Multivariate statistical process control methods for batch production: A review focused on applications. Production & manufacturing research, 9(1), 33–55. https://doi.org/10.1080/21693277.2020.1871441
[3] Sikder, S., Mukherjee, I., & Panja, S. C. (2020). A synergistic Mahalanobis--Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach. Journal of manufacturing systems, 57, 323–337. https://doi.org/10.1016/j.jmsy.2020.10.003
[4] Tegegne, D. A., Kitaw, D., & Berhan, E. (2022). Advances in statistical quality control chart techniques and their limitations to cement industry. Cogent engineering, 9(1), 2088463. https://doi.org/10.1080/23311916.2022.2088463
[5] Yao, Y., & Gao, F. (2009). A survey on multistage/multiphase statistical modeling methods for batch processes. Annual reviews in control, 33(2), 172–183. https://doi.org/10.1016/j.arcontrol.2009.08.001
[6] Ma, J., & Zhang, J. (2022). Progress of process monitoring for the multi-mode process: A review. Applied sciences, 12(14), 7207. https://www.mdpi.com/2076-3417/12/14/7207
[7] Jiang, X., Zhao, H., & Jin, B. (2015). Multimode process monitoring based on sparse principal component selection and bayesian inference-based probability. Mathematical problems in engineering, 2015(1), 465372. https://doi.org/10.1155/2015/465372
[8] Sabahno, H., & Niaki, S. T. A. (2023). New machine-learning control charts for simultaneous monitoring of multivariate normal process parameters with detection and identification. Mathematics, 11(16), 3566. https://doi.org/10.3390/math11163566
[9] Pilario, K. E., Shafiee, M., Cao, Y., Lao, L., & Yang, S.-H. (2019). A review of kernel methods for feature extraction in nonlinear process monitoring. Processes, 8(1), 24. https://www.mdpi.com/2227-9717/8/1/24
[10] Zhang, K., Peng, K., Zhao, S., & Wang, F. (2020). A novel feature-extraction-based process monitoring method for multimode processes with common features and its applications to a rolling process. IEEE transactions on industrial informatics, 17(9), 6466–6475. https://doi.org/10.1109/TII.2020.3012024
[11] Harkat, M.-F., Kouadri, A., Fezai, R., Mansouri, M., Nounou, H., & Nounou, M. (2020). Machine learning-based reduced kernel PCA model for nonlinear chemical process monitoring. Journal of control, automation and electrical systems, 31(5), 1196–1209. https://doi.org/10.1007/s40313-020-00604-w
[12] Guo, L., Wu, P., Lou, S., Gao, J., & Liu, Y. (2020). A multi-feature extraction technique based on principal component analysis for nonlinear dynamic process monitoring. Journal of process control, 85, 159–172. https://doi.org/10.1016/j.jprocont.2019.11.010
[13] Peng, G., Huang, K., & Wang, H. (2021). Dynamic multimode process monitoring using recursive GMM and KPCA in a hot rolling mill process. Systems science & control engineering, 9(1), 592–601. https://www.tandfonline.com/doi/abs/10.1080/21642583.2021.1967220
[14] Du, W., Fan, Y., & Zhang, Y. (2017). Multimode process monitoring based on data-driven method. Journal of the franklin institute, 354(6), 2613–2627. https://doi.org/10.1016/j.jfranklin.2016.11.002
[15] El-Midany, T. T., El-Baz, M. A., & Abd-Elwahed, M. S. (2010). A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks. Expert systems with applications, 37(2), 1035–1042. https://doi.org/10.1016/j.eswa.2009.05.092
[16] Li, T., Hu, S., Wei, Z., & Liao, Z. (2013). A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines. Mathematical problems in engineering, 2013(1), 494626. https://doi.org/10.1155/2013/494626
[17] Pouya, A., Yeganeh, A., & Fadaei, S. (2021). Designing a T2 hotelling control chart using clustering. Sharif industrial engineering and management journal, 37(1), 71-82. (In Persian). https://www.magiran.com/p2350284
[18] Ahmadi, S. (2021). Designing control charts using robust clustering [Thesis]. (In Persian). https://eng.khu.ac.ir/find-60.9760.64255.fa.html
[19] Ismail, M., Mostafa, N. A., & El-Assal, A. (2022). Quality monitoring in multistage manufacturing systems by using machine learning techniques. Journal of intelligent manufacturing, 33(8), 2471–2486. https://doi.org/10.1007/s10845-021-01792-1%0A%0A
[20] Lou, Z., Wang, Y., Si, Y., & Lu, S. (2022). A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysis. Automatica, 138, 110148. https://doi.org/10.1016/j.automatica.2021.110148
[21] Webb, Z. T., Nnadili, M., Seghers, E. E., Briceno-Mena, L. A., & Romagnoli, J. A. (2022). Optimization of multi-mode classification for process monitoring. Frontiers in chemical engineering, 4, 900083. https://doi.org/10.3389/fceng.2022.900083
[22] Moradi, M., & Zarei, S. (2024). Robust model-based clustering using a symmetric stable α-distribution for measurement error. Journal of statistical sciences, 18(1) .(In Persian). https://civilica.com/doc/2001221/
[23] Zhang, J., Zhou, D., & Chen, M. (2022). Self-learning sparse PCA for multimode process monitoring. IEEE transactions on industrial informatics, 19(1), 29–39. https://doi.org/10.1109/TII.2022.3178736
[24] Amiri, A. H., Maleki, M. R., & Doroudian, M. H. (2014). Monitoring variability in multivariate and attribute process using artificial neural networks. Journal of production and operations management, 5(2), 21-36. (In Persian). https://civilica.com/doc/1188241/