Statistical monitoring of fed-batch process using dynamic multiway neighborhood preserving embedding
暂无分享,去创建一个
Jingqi Yuan | Kunlun Hu | Jingqi Yuan | K. Hu
[1] A. J. Morris,et al. Non-linear principal components analysis for process fault detection , 1998 .
[2] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[3] Peter A Vanrolleghem,et al. Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis. , 2003, Biotechnology and bioengineering.
[4] Barry M. Wise,et al. The process chemometrics approach to process monitoring and fault detection , 1995 .
[5] ChangKyoo Yoo,et al. Dynamic Monitoring Method for Multiscale Fault Detection and Diagnosis in MSPC , 2002 .
[6] Manabu Kano,et al. Monitoring independent components for fault detection , 2003 .
[7] S. Joe Qin,et al. Statistical process monitoring: basics and beyond , 2003 .
[8] J. Macgregor,et al. Monitoring batch processes using multiway principal component analysis , 1994 .
[9] ChangKyoo Yoo,et al. Fault detection of batch processes using multiway kernel principal component analysis , 2004, Comput. Chem. Eng..
[10] Thomas E Marlin,et al. Process Control , 1995 .
[11] C. Croux,et al. Principal Component Analysis Based on Robust Estimators of the Covariance or Correlation Matrix: Influence Functions and Efficiencies , 2000 .
[12] ChangKyoo Yoo,et al. On-line monitoring of batch processes using multiway independent component analysis , 2004 .
[13] B. Bakshi. Multiscale PCA with application to multivariate statistical process monitoring , 1998 .
[14] Junghui Chen,et al. On-line batch process monitoring using dynamic PCA and dynamic PLS models , 2002 .
[15] Weihua Li,et al. Recursive PCA for adaptive process monitoring , 1999 .
[16] David M. Himmelblau,et al. Sensor Fault Detection via Multiscale Analysis and Dynamic PCA , 1999 .
[17] Jose A. Romagnoli,et al. Robust multi-scale principal components analysis with applications to process monitoring , 2005 .
[18] John F. MacGregor,et al. Process monitoring and diagnosis by multiblock PLS methods , 1994 .
[19] ChangKyoo Yoo,et al. On-line Batch Process Monitoring Using Different Unfolding Method and Independent Component Analysis , 2003 .
[20] In-Beum Lee,et al. Adaptive multivariate statistical process control for monitoring time-varying processes , 2006 .
[21] T. McAvoy,et al. Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .
[22] Christos Georgakis,et al. Disturbance detection and isolation by dynamic principal component analysis , 1995 .
[23] John F. MacGregor,et al. Multi-way partial least squares in monitoring batch processes , 1995 .
[24] ChangKyoo Yoo,et al. Statistical monitoring of dynamic processes based on dynamic independent component analysis , 2004 .
[25] Gang Chen,et al. Predictive on-line monitoring of continuous processes , 1998 .
[26] ChangKyoo Yoo,et al. Statistical process monitoring with independent component analysis , 2004 .
[27] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[28] John F. MacGregor,et al. Multivariate SPC charts for monitoring batch processes , 1995 .
[29] Jose A. Romagnoli,et al. A robust strategy for real-time process monitoring , 2001 .
[30] Theodora Kourti,et al. Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS , 1995 .
[31] Age K. Smilde,et al. Generalized contribution plots in multivariate statistical process monitoring , 2000 .
[32] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[33] John F. MacGregor,et al. Adaptive batch monitoring using hierarchical PCA , 1998 .
[34] G. Irwin,et al. Process monitoring approach using fast moving window PCA , 2005 .