Nonlinear and Non-Gaussian Dynamic Batch Process Monitoring Using a New Multiway Kernel Independent Component Analysis and Multidimensional Mutual Information Based Dissimilarity Approach

Batch or semibatch process monitoring is a challenging task because of various factors such as strong nonlinearity, inherent time-varying dynamics, batch-to-batch variations, and multiple operating phases. In this article, a novel nonlinear and non-Gaussian dissimilarity method based on multiway kernel independent component analysis (MKICA) and multidimensional mutual information (MMI) is developed and applied to batch process monitoring and abnormal event detection. MKICA models are first built on the normal benchmark and monitored batches to characterize the nonlinear and non-Gaussian variable relationship of batch processes. Then, the kernel independent component (IC) subspaces are extracted from the benchmark and monitored batches. Further, a multidimensional mutual information based dissimilarity index is defined to quantitatively evaluate the statistical dependence between the benchmark and monitored subspaces through the moving-window strategy. With the corresponding control limit estimated from th...

[1]  Alexander Kraskov,et al.  Published under the scientific responsability of the EUROPEAN PHYSICAL SOCIETY Incorporating , 2002 .

[2]  S. Joe Qin,et al.  Variance component analysis based fault diagnosis of multi-layer overlay lithography processes , 2009 .

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

[4]  Karlene A. Kosanovich,et al.  Improved Process Understanding Using Multiway Principal Component Analysis , 1996 .

[5]  S. Qin,et al.  Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .

[6]  P. A. Taylor,et al.  Synchronization of batch trajectories using dynamic time warping , 1998 .

[7]  A. Çinar,et al.  Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis , 2003 .

[8]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[9]  Jie Yu A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes , 2012 .

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

[11]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[12]  John F. MacGregor,et al.  Multi-way partial least squares in monitoring batch processes , 1995 .

[13]  A. J. Morris,et al.  Non-parametric confidence bounds for process performance monitoring charts☆ , 1996 .

[14]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[15]  S. Wold,et al.  Multi‐way principal components‐and PLS‐analysis , 1987 .

[16]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  S. Joe Qin,et al.  Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis , 2007 .

[18]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[19]  X. Wang,et al.  Statistical Process Control Charts for Batch Operations Based on Independent Component Analysis , 2004 .

[20]  Gülnur Birol,et al.  A modular simulation package for fed-batch fermentation: penicillin production , 2002 .

[21]  In-Beum Lee,et al.  Fault detection and diagnosis based on modified independent component analysis , 2006 .

[22]  Jie Yu,et al.  Nonlinear Bioprocess Monitoring Using Multiway Kernel Localized Fisher Discriminant Analysis , 2011 .

[23]  Junghui Chen,et al.  Development of hidden semi-Markov models for diagnosis of multiphase batch operation , 2011 .

[24]  Riccardo Leardi,et al.  Industrial experiences with multivariate statistical analysis of batch process data , 2006 .

[25]  Fuli Wang,et al.  Sub-PCA Modeling and On-line Monitoring Strategy for Batch Processes (R&D Note) , 2004 .

[26]  Bhavik R. Bakshi,et al.  Analysis of operating data for evaluation, diagnosis and control of batch operations , 1994 .

[27]  S. Qin,et al.  Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring , 2009 .

[28]  B Lennox,et al.  Process monitoring of an industrial fed-batch fermentation. , 2001, Biotechnology and bioengineering.

[29]  Jie Yu,et al.  Localized Fisher discriminant analysis based complex chemical process monitoring , 2011 .