Online Monitoring of Multivariate Processes Using Higher-Order Cumulants Analysis

In this study, a novel approach is developed for online state monitoring based on higher-order cumulants analysis (HCA). This approach applies higher-order cumulants to monitor a multivariate process, and while conventional approaches such as independent components analysis (ICA) uses variance to monitor process. Variance is lower-order statistics and is only sensitive to amplitude. In contrast, higher-order cumulants, the typical higher-order statistics, carry important information and are sensitive to both amplitude and phase, particularly for non-Gaussian distributions. The main idea of this novel approach is to monitor the cumulants of dominant independent components and residuals of the ICA model. Therefore, higher-order statistical information of multivariate processes can be monitored online. Furthermore, a variable contribution analysis scheme is developed for HCA to diagnose faults. The proposed approach is applied to the Tennessee Eastman (TE) process to exhibit its effectiveness. The results de...

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

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

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

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

[5]  S. Qin,et al.  Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .

[6]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

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

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

[9]  G.B. Giannakis,et al.  Cumulant-based order determination of non-Gaussian ARMA models , 1990, IEEE Trans. Acoust. Speech Signal Process..

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

[11]  Mudassir M. Rashid,et al.  A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-Gaussian dynamic process monitoring , 2012 .

[12]  Xiaobo Chen,et al.  Multivariate industrial process monitoring based on the integration method of canonical variate analysis and independent component analysis , 2012 .

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

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

[15]  Donghua Zhou,et al.  Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process , 2011, IEEE Transactions on Control Systems Technology.

[16]  Chun-Chin Hsu,et al.  Adaptive Kernel Principal Component Analysis (KPCA) for Monitoring Small Disturbances of Nonlinear Processes , 2010 .

[17]  Brian M. Sadler,et al.  Estimation and detection in non-Gaussian noise using higher order statistics , 1994, IEEE Trans. Signal Process..

[18]  Yingwei Zhang,et al.  Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM , 2009 .

[19]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[20]  Hector Budman,et al.  Fault detection, identification and diagnosis using CUSUM based PCA , 2011 .

[21]  Junghui Chen,et al.  Dynamic process fault monitoring based on neural network and PCA , 2002 .

[22]  Christos Georgakis,et al.  Plant-wide control of the Tennessee Eastman problem , 1995 .

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

[24]  S. Qin,et al.  Combined Indices for ICA and Their Applications to Multivariate Process Fault Diagnosis: Combined Indices for ICA and Their Applications to Multivariate Process Fault Diagnosis , 2014 .

[25]  Nina F. Thornhill,et al.  Diagnosis of poor control-loop performance using higher-order statistics , 2004, Autom..

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

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

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

[29]  Mudassir M. Rashid,et al.  Hidden Markov Model Based Adaptive Independent Component Analysis Approach for Complex Chemical Process Monitoring and Fault Detection , 2012 .

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

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

[32]  S. Joe Qin,et al.  Analysis and generalization of fault diagnosis methods for process monitoring , 2011 .

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

[34]  Donghua Zhou,et al.  Total projection to latent structures for process monitoring , 2009 .

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

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

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

[38]  Georgios B. Giannakis,et al.  Signal detection and classification using matched filtering and higher order statistics , 1989, IEEE Trans. Acoust. Speech Signal Process..

[39]  A. Ben Hamza,et al.  Dynamic independent component analysis approach for fault detection and diagnosis , 2010, Expert Syst. Appl..

[40]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[41]  Hui Cao,et al.  Dimension reduction method of independent component analysis for process monitoring based on minimum mean square error , 2012 .