A Novel Strategy of the Data Characteristics Test for Selecting a Process Monitoring Method Automatically

Process monitoring of industrial processes has attracted broad attention in recent years. Many approaches, such as PCA, KICA, and so forth, have been proposed and successfully applied in many fields. However, the assumption of data characteristics for each method has significantly limited its application. PCA is more suitable for Gaussian processes, whereas ICA is always used in non-Gaussian processes. ICA-PCA has been proposed to analyze some complex processes that contain both Gaussian and non-Gaussian information, and Kernel-based methods (i.e., KPCA, KICA, and kernel ICA-PCA) have been shown to be more effective in nonlinear processes. Therefore, data characteristics should be known to select the most suitable monitoring method. However, prior knowledge is in fact hard to obtain in most industrial processes. For coping with the problem, a novel strategy that can test the data characteristics has been proposed in this paper to select an appropriate monitoring method automatically. A novel nonlinearity ...

[1]  U. Kruger,et al.  Moving window kernel PCA for adaptive monitoring of nonlinear processes , 2009 .

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

[3]  Chunhui Zhao,et al.  Nonlinear Batch Process Monitoring Using Phase-Based Kernel-Independent Component Analysis-Principal Component Analysis (KICA-PCA) , 2009 .

[4]  Fuli Wang,et al.  Multivariate process monitoring based on the distribution test of the data , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

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

[6]  N. J. H. Small.  Marginal Skewness and Kurtosis in Testing Multivariate Normality , 1980 .

[7]  M. Srivastava,et al.  On assessing multivariate normality based on shapiro-wilk W statistic , 1987 .

[8]  Fuli Wang,et al.  Nonlinear process monitoring based on kernel dissimilarity analysis , 2009 .

[9]  G. Rong,et al.  Learning a data-dependent kernel function for KPCA-based nonlinear process monitoring , 2009 .

[10]  In-Beum Lee,et al.  Fault Detection of Non-Linear Processes Using Kernel Independent Component Analysis , 2008 .

[11]  Zhiqiang Ge,et al.  A Nonlinear Probabilistic Method for Process Monitoring , 2010 .

[12]  S. Saigal,et al.  Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Jie Yu,et al.  Independent Component Analysis Mixture Model Based Dissimilarity Method for Performance Monitoring of Non-Gaussian Dynamic Processes with Shifting Operating Conditions , 2014 .

[14]  Fuli Wang,et al.  Multimode Process Monitoring Based on Mode Identification , 2012 .

[15]  A note on Srivastava and Hui's tests of multivariate normality , 2008 .

[16]  R. Penrose.  A Generalized inverse for matrices , 1955 .

[17]  Barry Lennox,et al.  Monitoring a complex refining process using multivariate statistics , 2008 .

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

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

[20]  Q. Wang,et al.  A nonlinear correlation measure for multivariable data set , 2005 .

[21]  Jin Hyun Park,et al.  Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .

[22]  Mark A. Kramer,et al.  Autoassociative neural networks , 1992 .

[23]  Muni S. Srivastava,et al.  A measure of skewness and kurtosis and a graphical method for assessing multivariate normality , 1984 .

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

[25]  J. Edward Jackson,et al.  A User's Guide to Principal Components: Jackson/User's Guide to Principal Components , 2004 .

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

[27]  P. Rapp,et al.  Statistical validation of mutual information calculations: comparison of alternative numerical algorithms. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Manabu Kano,et al.  A new multivariate statistical process monitoring method using principal component analysis , 2001 .

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

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

[31]  Jicong Fan,et al.  Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA , 2014 .

[32]  Moon,et al.  Estimation of mutual information using kernel density estimators. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[33]  Medhat Ahmed Rakha,et al.  On the Moore-Penrose generalized inverse matrix , 2004, Appl. Math. Comput..

[34]  T. McAvoy,et al.  Nonlinear principal component analysis—Based on principal curves and neural networks , 1996 .

[35]  George W. Irwin,et al.  Introduction of a nonlinearity measure for principal component models , 2005, Comput. Chem. Eng..