Robust Online Monitoring Based on Spherical-Kernel Partial Least Squares for Nonlinear Processes with Contaminated Modeling Data

KPLS is a very efficient technique for tackling complex nonlinear data sets by mapping an original input space into a high-dimensional feature space. However, KPLS may not function well when the model set is contaminated to a large extent by outliers. In this Article, a robust version of kernel partial least squares (KPLS) called spherical kernel partial least squares (SKPLS) is introduced for monitoring nonlinear processes. The key idea of SKPLS is to project all of the feature vectors in the feature space onto a unit sphere and then to perform KPLS on the sphered feature vectors. The effects of the outliers in the original data are eliminated or diminished because of the sphering. The robust monitoring statistics and robust control limits are derived for process monitoring purposes. The simulation results show that the proposed process monitoring strategy not only works well when the modeling data set does not contain outliers but also offers a satisfactory efficiency when the modeling data set is highl...

[1]  Ying-wei Zhang,et al.  On-line batch process monitoring using hierarchical kernel partial least squares , 2011 .

[2]  Hae Woo Lee,et al.  Multi-scale extension of PLS algorithm for advanced on-line process monitoring , 2009 .

[3]  D. G. Simpson,et al.  Robust principal component analysis for functional data , 2007 .

[4]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

[5]  P. Rousseeuw,et al.  A fast algorithm for the minimum covariance determinant estimator , 1999 .

[6]  Yingwei Zhang,et al.  Multivariate process monitoring and analysis based on multi-scale KPLS , 2011 .

[7]  Jose A. Romagnoli,et al.  Robust statistical process monitoring , 1996 .

[8]  Beata Walczak,et al.  Robust SIMCA-bounding influence of outliers , 2007 .

[9]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[10]  P. Rousseeuw,et al.  Alternatives to the Median Absolute Deviation , 1993 .

[11]  David J. Sandoz,et al.  Extended PLS approach for enhanced condition monitoring of industrial processes , 2001 .

[12]  Ying-wei Zhang,et al.  Complex process quality prediction using modified kernel partial least squares , 2010 .

[13]  Roman Rosipal,et al.  Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..

[14]  Theodora Kourti,et al.  Statistical Process Control of Multivariate Processes , 1994 .

[15]  Ying-wei Zhang,et al.  Combining Kernel Partial Least-Squares Modeling and Iterative Learning Control for the Batch-to-Batch Optimization of Constrained Nonlinear Processes , 2010 .

[16]  Xi Zhang,et al.  Nonlinear Multivariate Quality Estimation and Prediction Based on Kernel Partial Least Squares , 2008 .

[17]  Zhiqiang Ge,et al.  Improved kernel PCA-based monitoring approach for nonlinear processes , 2009 .

[18]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[19]  Jie Zhang,et al.  Nonlinear projection to latent structures method and its applications , 2006 .

[20]  Zhiming Li,et al.  Modeling and Monitoring of Dynamic Processes , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[21]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[22]  Tim Verdonck,et al.  Robust kernel principal component analysis and classification , 2010, Adv. Data Anal. Classif..

[23]  Mia Hubert,et al.  Detecting influential observations in Kernel PCA , 2010, Comput. Stat. Data Anal..

[24]  Jose A. Romagnoli,et al.  A robust strategy for real-time process monitoring , 2001 .

[25]  In-Beum Lee,et al.  A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction , 2005 .

[26]  Zhiqiang Ge,et al.  Robust Online Monitoring for Multimode Processes Based on Nonlinear External Analysis , 2008 .

[27]  Hong Zhou,et al.  Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares , 2010, IEEE Transactions on Industrial Informatics.

[28]  Jose A. Romagnoli,et al.  Robust multi-scale principal components analysis with applications to process monitoring , 2005 .

[29]  Chi Ma,et al.  Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS , 2011 .

[30]  Carlos F. Alcala,et al.  Reconstruction-based contribution for process monitoring with kernel principal component analysis , 2010, Proceedings of the 2010 American Control Conference.

[31]  Zhizhong Mao,et al.  Kernel partial robust M-regression as a flexible robust nonlinear modeling technique , 2010 .

[32]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[33]  Ying-wei Zhang,et al.  Complex process monitoring using modified partial least squares method of independent component regression , 2009 .

[34]  S. D. Jong.  SIMPLS: an alternative approach to partial least squares regression , 1993 .