Dong, D., & McAvoy, T. J. (1996). Nonlinear principal component analysis—Based on principal curves and neural networks. Computers & Chemical Engineering, 20(1), 65-78. doi:10.1016/0098-1354(95)00003-k
Fourie, S. H., & de Vaal, P. (2000). Advanced process monitoring using an on-line non-linear multiscale principal component analysis methodology. Computers & Chemical Engineering, 24(2-7), 755-760. doi:10.1016/s0098-1354(00)00417-8
Fuente, M., Garcia, G., Sainz, G., 2008. Fault diagnosis in a plant using fisher discriminant analysis. Proceding of the 16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France, 53-58.
[+]
Dong, D., & McAvoy, T. J. (1996). Nonlinear principal component analysis—Based on principal curves and neural networks. Computers & Chemical Engineering, 20(1), 65-78. doi:10.1016/0098-1354(95)00003-k
Fourie, S. H., & de Vaal, P. (2000). Advanced process monitoring using an on-line non-linear multiscale principal component analysis methodology. Computers & Chemical Engineering, 24(2-7), 755-760. doi:10.1016/s0098-1354(00)00417-8
Fuente, M., Garcia, G., Sainz, G., 2008. Fault diagnosis in a plant using fisher discriminant analysis. Proceding of the 16th Mediterranean Conference on Control and Automation Congress Centre, Ajaccio, France, 53-58.
Garcia-Alvarez, D., Fuente, M., 2008. Analisis comparativo de tecnicas de deteccion de fallos utilizando analisis de componentes principales (pca). Proceding of the 29th Spanish Conference on Automation, Tarragona, Spain.
Jackson, J. E., & Mudholkar, G. S. (1979). Control Procedures for Residuals Associated With Principal Component Analysis. Technometrics, 21(3), 341-349. doi:10.1080/00401706.1979.10489779
Kano, M., Hasebe, S., Hashimoto, I., & Ohno, H. (2004). Evolution of multivariate statistical process control: application of independent component analysis and external analysis. Computers & Chemical Engineering, 28(6-7), 1157-1166. doi:10.1016/j.compchemeng.2003.09.011
Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859
Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699
Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE Journal, 37(2), 233-243. doi:10.1002/aic.690370209
Kramer, M. A. (1992). Autoassociative neural networks. Computers & Chemical Engineering, 16(4), 313-328. doi:10.1016/0098-1354(92)80051-a
Lane, S., Martin, E. B., Morris, A. J., & Gower, P. (2003). Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process. Transactions of the Institute of Measurement and Control, 25(1), 17-35. doi:10.1191/0142331203tm071oa
MacGregor, J. F., & Kourti, T. (1995). Statistical process control of multivariate processes. Control Engineering Practice, 3(3), 403-414. doi:10.1016/0967-0661(95)00014-l
Misra, M., Yue, H. H., Qin, S. J., & Ling, C. (2002). Multivariate process monitoring and fault diagnosis by multi-scale PCA. Computers & Chemical Engineering, 26(9), 1281-1293. doi:10.1016/s0098-1354(02)00093-5
Puigjaner, L., Ollero, P., Prada, C., Jiménez, L., 2006. Estrategias de modelado, simulación y optimización de procesos químicos. Editorial Sintesis.
Shlens, J., 2005. A tutorial on principal component analysis. La Jolla, CA 92037: Salk Institute for Biological Studies.
Tan, S., & Mayrovouniotis, M. L. (1995). Reducing data dimensionality through optimizing neural network inputs. AIChE Journal, 41(6), 1471-1480. doi:10.1002/aic.690410612
Tien, D., Lim, K., Jun, L., November 2-6 2004. Compartive study of pca approaches in process monitoring and fault detection. The 30th annual conference of the IEEE industrial electronics society, 2594-2599.
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis. Computers & Chemical Engineering, 27(3), 293-311. doi:10.1016/s0098-1354(02)00160-6
Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis. Computers & Chemical Engineering, 27(3), 313-326. doi:10.1016/s0098-1354(02)00161-8
Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis. Computers & Chemical Engineering, 27(3), 327-346. doi:10.1016/s0098-1354(02)00162-x
Weighell, M., Martin, E. B., & Morris, A. J. (2001). The statistical monitoring of a complex manufacturing process. Journal of Applied Statistics, 28(3-4), 409-425. doi:10.1080/02664760120034144
Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1-3), 37-52. doi:10.1016/0169-7439(87)80084-9
Zarzo, M., 2004. Aplicación de técnicas estadísticas multivariantes al control de la calidad de procesos por lotes. Ph.D. thesis, Universidad Politécnica de Valencia.
Zumoffen, D., & Basualdo, M. (2008). From Large Chemical Plant Data to Fault Diagnosis Integrated to Decentralized Fault-Tolerant Control: Pulp Mill Process Application. Industrial & Engineering Chemistry Research, 47(4), 1201-1220. doi:10.1021/ie071064m
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