References
- Chen, Q., Wynne, R. J., Goulding, P., & Sandoz, D. (2000). The application of principal component analysis and kernel density estimation to enhance process monitoring. Control Engineering Practice, 8(5), 531–543. https://doi.org/10.1016/S0967-0661(99)00191-4
- Chiang, L., Russell, E., & Braatz, R. (2001). Fault detection and diagnosis in industrial systems. Springer-Verlag.
- Choi, S. W., Lee, C., Lee, J.-M., Park, J. H., & Lee, I.-B. (2005). Fault detection and identification of nonlinear processes based on kernel PCA. Chemometrics and Intelligent Laboratory Systems, 75(1), 55–67. https://doi.org/10.1016/j.chemolab.2004.05.001
- Deng, X., Tian, X., & Chen, S. (2013). Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis. Chemometrics and Intelligent Laboratory Systems, 127, 195–209. https://doi.org/10.1016/j.chemolab.2013.07.001
- Dong, D., & McAvoy, T. J. (1996). Nonlinear principal component analysis based on principal curves and neural networks. Computers & Chemical Engineering, 20(1), 65–78. https://doi.org/10.1016/0098-1354(95)00003-K
- Downs, J., & Vogel, E. (1993). A plant-wide industrial process control problem. Computers & Chemical Engineering, 17(3), 245–255. https://doi.org/10.1016/0098-1354(93)80018-I
- Ge, Z., & Song, Z. (2013). Multivariate statistical process control: Process monitoring methods and applications. Springer-Verlag.
- Ge, Z., Song, Z., & Gao, F. (2013). Review of recent research on data-based process monitoring. Industrial & Engineering Chemistry Research, 52(10), 3543–3562. https://doi.org/10.1021/ie302069q
- Jiang, Q., & Yan, X. (2013). Statistical monitoring of chemical processes based on sensitive kernel principal components. Process System Engineering and Process Safety, 21(6), 633–643. https://doi.org/10.1016/S1004-9541(13)60506-6
- Jiang, Q., & Yan, X. (2018). Parallel PCA-KPCA for nonlinear process monitoring. Control Engineering Practice, 80, 17–25. https://doi.org/10.1016/j.conengprac.2018.07.012
- Jolliffe, I. (2002). Principal component analysis (2nd ed.). Springer-Verlag.
- Kramer, M. A. (1992). Autoassociative neural networks. Computers & Chemical Engineering, 16(4), 313–328. https://doi.org/10.1016/0098-1354(92)80051-A
- Kraskov, A., Stögbauer, H., Andrzejak, R. G., & Grassberger, P. (2005). Hierarchical clustering using mutual information. Europhysics Letters, 70, 78. https://doi.org/10.1209/epl/i2004-10483-y
- Lee, J. M., Yoo, C. K., Choi, S. W., Vanrolleghem, P. A., & I. B. Lee (2004). Nonlinear process monitoring using kernel principal component analysis. Chemical Engineering Science, 59(1), 223–234. https://doi.org/10.1016/j.ces.2003.09.012
- Li, Y. F., Wang, Z. F., & Yuan, J. Q. (2006). On-line fault detection using SVM-based dynamic MPLS for batch processes. Chinese Journal of Chemical Engineering, 21(6), 633–643. https://doi.org/10.1016/s1004-9541(07)60007-x
- Liang, J. (2005). Multivariate statistical process monitoring using kernel density estimation. Developments in Chemical Engineering and Mineral Processing, 13(1–2), 185–192. https://doi.org/10.1002/apj.5500130117
- McAvoy, T. J., & Ye, N. (1994). Base control for the Tennessee Eastman problem. Computers & Chemical Engineering, 18(5), 383–413. https://doi.org/10.1016/0098-1354(94)88019-0
- Mika, S., Schölkopf, B., Smola, A., Müller, K. R., Scholz, M., & Rätsch, G. (1999). Kernel PCA and de-noising in feature spaces. Advances in Neural Information Processing System, 11, 536–542. https://dl.acm.org/doi/10.5555/340534.340729
- Odiowei, P. E. P., & Cao, Y. (2010). Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations. IEEE Transactions on Industrial Informatics, 6(1), 36–44. https://doi.org/10.1109/TII.2009.2032654
- Russell, E., Chiang, L., & Braatz, R. (2000). Data-driven methods for fault detection and diagnosis in chemical processes. Springer-Verlag. Chemometrics and intelligent laboratory systems.
- Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Non-linear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319. https://doi.org/10.1162/089976698300017467
- Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1–3), 37–52. https://doi.org/10.1016/0169-7439(87)80084-9
- Yin, S., Ding, S. X., Xie, X. S., & Luo, H. (2014). A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics, 61(11), 6418–6428. https://doi.org/10.1109/TIE.2014.2301773
- Yin, S., Li, X., Gao, H., & Kaynak, O. (2015). Data-based techniques focused on modern industry: An overview. IEEE Transactions on Industrial Electronics, 62(1), 657–667. https://doi.org/10.1109/TIE.41