Large‐scale dynamic process monitoring based on performance‐driven distributed canonical variate analysis

As a typical process monitoring method for the large‐scale industrial process, the distributed principal components analysis (DPCA) needs to be improved because of its rough selection for the variables in each subblock. Moreover, for DPCA, the process dynamic property is ignored and invalid fault diagnosis may occur. Therefore, a performance‐driven distributed canonical variate analysis (DCVA) is proposed. Firstly, with historical fault information, the genetic algorithm is utilized to select appropriate variables for each subblock; secondly, canonical variate analysis is introduced to capture the dynamic information for performance improvement; finally, a novel fault diagnosis method is developed for the DCVA model. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the effectiveness of the proposed model.

[1]  Zhi-huan Song,et al.  Multimode Dynamic Process Monitoring Based on Mixture Canonical Variate Analysis Model , 2015 .

[2]  Michael J. Piovoso,et al.  On unifying multiblock analysis with application to decentralized process monitoring , 2001 .

[3]  Qiang Liu,et al.  Multiblock Concurrent PLS for Decentralized Monitoring of Continuous Annealing Processes , 2014, IEEE Transactions on Industrial Electronics.

[4]  A. Negiz,et al.  Statistical monitoring of multivariable dynamic processes with state-space models , 1997 .

[5]  Dexian Huang,et al.  Canonical variate analysis-based contributions for fault identification , 2015 .

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

[7]  Steven X. Ding,et al.  Data-driven design of monitoring and diagnosis systems for dynamic processes: A review of subspace technique based schemes and some recent results , 2014 .

[8]  John F. MacGregor,et al.  Process monitoring and diagnosis by multiblock PLS methods , 1994 .

[9]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.

[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]  S. Joe Qin,et al.  Consistent dynamic PCA based on errors-in-variables subspace identification , 2001 .

[12]  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.

[13]  Hans-Georg Beyer.  The simple genetic algorithm-foundations and theory [Book Reviews] , 2000, IEEE Transactions on Evolutionary Computation.

[14]  Richard D. Braatz,et al.  Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis , 2000 .

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

[16]  Thomas J. McAvoy,et al.  Fault Detection and Diagnosis in Industrial Systems , 2002 .

[17]  George W. Irwin,et al.  Improved principal component monitoring of large-scale processes , 2004 .

[18]  Kaushik Ghosh,et al.  Optimal variable selection for effective statistical process monitoring , 2014, Comput. Chem. Eng..

[19]  In-Beum Lee,et al.  Multiblock PLS-based localized process diagnosis , 2005 .

[20]  Lei Xie,et al.  Statistical Monitoring of Dynamic Multivariate Processes - Part 1. Modeling Autocorrelation and Cross-correlation , 2006 .

[21]  Ali Cinar,et al.  Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring , 2000 .

[22]  Zhi-huan Song,et al.  Distributed PCA Model for Plant-Wide Process Monitoring , 2013 .

[23]  Juan Li,et al.  Performance-driven optimal design of distributed monitoring for large-scale nonlinear processes , 2016 .

[24]  J. Macgregor,et al.  Analysis of multiblock and hierarchical PCA and PLS models , 1998 .

[25]  Donghua Zhou,et al.  A New Method of Dynamic Latent-Variable Modeling for Process Monitoring , 2014, IEEE Transactions on Industrial Electronics.

[26]  Yi Cao,et al.  Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection , 2018, IEEE Transactions on Industrial Informatics.

[27]  Richard D. Braatz,et al.  A combined canonical variate analysis and Fisher discriminant analysis (CVA-FDA) approach for fault diagnosis , 2015, Comput. Chem. Eng..

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