Slow feature analysis based on online feature reordering and feature selection for dynamic chemical process monitoring

Abstract This study considers the insufficiency of traditional monitoring methods to eliminate dynamics, and proposes a novel online feature reordering- and feature selection-based slow feature analysis (SFA) algorithm. The SFA algorithm explores the process dynamics from the view of inner variation of data to extract the slowly varying features. The extracted SFs are considered as the representations of steady- and dynamic-state processes. Online feature reordering and feature selection strategies maximize online fault information and can be used to perform fault detection operation. The proposed method is applied to two simulated processes. Monitoring results show that the proposed method has better monitoring results than those of traditional methods.

[1]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[2]  ChangKyoo Yoo,et al.  Statistical monitoring of dynamic processes based on dynamic independent component analysis , 2004 .

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

[4]  Xuefeng Yan,et al.  Gaussian and non-Gaussian Double Subspace Statistical Process Monitoring Based on Principal Component Analysis and Independent Component Analysis , 2015 .

[5]  Laurenz Wiskott,et al.  What Is the Relation Between Slow Feature Analysis and Independent Component Analysis? , 2006, Neural Computation.

[6]  Jian Huang,et al.  Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference , 2015 .

[7]  Biao Huang,et al.  Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference , 2016, IEEE Transactions on Industrial Electronics.

[8]  Christos Georgakis,et al.  Disturbance detection and isolation by dynamic principal component analysis , 1995 .

[9]  Gang Rong,et al.  Fault Isolation by Partial Dynamic Principal Component Analysis in Dynamic Process , 2006 .

[10]  Xuefeng Yan,et al.  Quality Relevant and Independent Two Block Monitoring Based on Mutual Information and KPCA , 2017, IEEE Transactions on Industrial Electronics.

[11]  Huijun Gao,et al.  Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures , 2016, IEEE Transactions on Control Systems Technology.

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

[13]  X. Wang,et al.  Dimension reduction of process dynamic trends using independent component analysis , 2002 .

[14]  Yingwei Zhang,et al.  Fault Detection and Diagnosis of Nonlinear Processes Using Improved Kernel Independent Component Analysis (KICA) and Support Vector Machine (SVM) , 2008 .

[15]  In-Beum Lee,et al.  Nonlinear dynamic process monitoring based on dynamic kernel PCA , 2004 .

[16]  Jicong Fan,et al.  Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis , 2014, Inf. Sci..

[17]  Laurenz Wiskott,et al.  Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.

[18]  Laurenz Wiskott,et al.  Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation , 2013, IEEE Transactions on Image Processing.

[19]  Octave Levenspiel,et al.  Fluid dispersion-generalization and comparison of mathematical models—I generalization of models , 1962 .

[20]  Jin Wang,et al.  Multivariate Statistical Process Monitoring Based on Statistics Pattern Analysis , 2010 .

[21]  G. Rong,et al.  Generalized orthogonal locality preserving projections for nonlinear fault detection and diagnosis , 2009 .

[22]  Xuefeng Yan,et al.  Plant-wide process monitoring based on mutual information-multiblock principal component analysis. , 2014, ISA transactions.

[23]  Jian Hou,et al.  Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes , 2016, Neurocomputing.

[24]  Junghui Chen,et al.  On-line batch process monitoring using dynamic PCA and dynamic PLS models , 2002 .

[25]  Fuli Wang,et al.  On-line batch process monitoring using batch dynamic kernel principal component analysis , 2010 .

[26]  Seongkyu Yoon,et al.  Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .

[27]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[28]  Mohd Azlan Hussain,et al.  Fault diagnosis of Tennessee Eastman process with multi- scale PCA and ANFIS , 2013 .

[29]  Dexian Huang,et al.  Monitoring of operating point and process dynamics via probabilistic slow feature analysis , 2016 .

[30]  Zhi-huan Song,et al.  Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors , 2007 .

[31]  Laurenz Wiskott,et al.  Applying Slow Feature Analysis to Image Sequences Yields a Rich Repertoire of Complex Cell Properties , 2002, ICANN.

[32]  Chen Jing,et al.  SVM and PCA based fault classification approaches for complicated industrial process , 2015, Neurocomputing.

[33]  Hanyuan Zhang,et al.  Batch Process Monitoring Based on Multiway Global Preserving Kernel Slow Feature Analysis , 2017, IEEE Access.

[34]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[35]  Dexian Huang,et al.  Slow feature analysis for monitoring and diagnosis of control performance , 2016 .

[36]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[37]  Laurenz Wiskott,et al.  Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses , 2003, Neural Computation.

[38]  Laurenz Wiskott,et al.  Independent Slow Feature Analysis and Nonlinear Blind Source Separation , 2004, Neural Computation.