Real-time monitoring of chemical processes based on variation information of principal component analysis model

In industrial processes, the change of operating condition can obviously affect the relations among process data, which in turn indicate the corresponding operating conditions. Considering that the loadings and eigenvalues, generated from the principal component analysis (PCA) model, contain primary data information and can reflect the characteristics of data, this article proposes novel monitoring statistics which quantitatively evaluate the variation of these two matrices, collected from real-time updated PCA model for process monitoring. Given that abnormal data may be submerged by normal data, a combined moving window which selects both real-time data and normal data is employed to collect data for model construction. By comparing with other PCA-based and non-PCA-based methods through a simple numerical simulation and the Tennessee Eastman process, the proposed data-driven method is demonstrated to be effective and feasible. Additionally, some other PCA-based methods are utilized for comparison.

[1]  H. Abdi,et al.  Principal component analysis , 2010 .

[2]  Koichi Fujiwara,et al.  Virtual sensing technology in process industries: Trends and challenges revealed by recent industria , 2013 .

[3]  Zhiqiang Ge,et al.  Nonlinear process monitoring based on linear subspace and Bayesian inference , 2010 .

[4]  Xuefeng Yan,et al.  Generalized Dice's coefficient‐based multi‐block principal component analysis with Bayesian inference for plant‐wide process monitoring , 2015 .

[5]  Peng Xu,et al.  Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion , 2012 .

[6]  Isao Noda,et al.  Moving Window Principal Component Analysis for Detecting Positional Fluctuation of Spectral Changes , 2011 .

[7]  Jon Dattorro,et al.  Equality relating Euclidean distance cone to positive semidefinite cone , 2008 .

[8]  Wolfgang Härdle,et al.  Principal Component Analysis , 2015 .

[9]  Manabu Kano,et al.  Comparison of statistical process monitoring methods: application to the Eastman challenge problem , 2000 .

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

[11]  Ali Ahmadi,et al.  Improvement of reliability in banknote classification using reject option and local PCA , 2004, Inf. Sci..

[12]  Hermann Kopetz,et al.  Real-time systems , 2018, CSC '73.

[13]  Mengling Wang,et al.  Adaptive Local Outlier Probability for Dynamic Process Monitoring , 2014 .

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

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

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

[17]  Wen-An Yang.  Monitoring and diagnosing of mean shifts in multivariate manufacturing processes using two-level selective ensemble of learning vector quantization neural networks , 2015, J. Intell. Manuf..

[18]  G. Irwin,et al.  Process monitoring approach using fast moving window PCA , 2005 .

[19]  Dale E. Seborg,et al.  Evaluation of a pattern matching method for the Tennessee Eastman challenge process , 2006 .

[20]  Lv Zhaomin,et al.  Batch Process Monitoring Based on Multisubspace Multiway Principal Component Analysis and Time-Series Bayesian Inference , 2014 .

[21]  Xuefeng Yan,et al.  Double-Weighted Independent Component Analysis for Non-Gaussian Chemical Process Monitoring , 2013 .

[22]  Dawei Zhao,et al.  Multi-objective optimal design of small scale resistance spot welding process with principal component analysis and response surface methodology , 2014, J. Intell. Manuf..

[23]  Alexander N Gorban,et al.  Multiscale principal component analysis , 2014 .

[24]  Min-Sen Chiu,et al.  Nonlinear process monitoring using JITL-PCA , 2005 .

[25]  Pierantonio Facco,et al.  Multivariate statistical real-time monitoring of an industrial fed-batch process for the production of specialty chemicals , 2009 .

[26]  Xue-feng Yan,et al.  Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis , 2013 .

[27]  Raghunathan Rengaswamy,et al.  Fault Diagnosis by Qualitative Trend Analysis of the Principal Components , 2005 .

[28]  Jaime A. Camelio,et al.  Statistical process monitoring approach for high-density point clouds , 2012, Journal of Intelligent Manufacturing.

[29]  Jialin Liu,et al.  Development of Self-Validating Soft Sensors Using Fast Moving Window Partial Least Squares , 2010 .

[30]  Sun-Yuan Kung,et al.  Principal Component Neural Networks: Theory and Applications , 1996 .

[31]  Weihua Li,et al.  Recursive PCA for adaptive process monitoring , 1999 .

[32]  Julian Morris,et al.  Progressive multi-block modelling for enhanced fault isolation in batch processes , 2014 .

[33]  José Camacho,et al.  Cross‐validation in PCA models with the element‐wise k‐fold (ekf) algorithm: theoretical aspects , 2012 .

[34]  Xuefeng Yan,et al.  Fault detection and identification using a Kullback-Leibler divergence based multi-block principal component analysis and bayesian inference , 2014, Korean Journal of Chemical Engineering.

[35]  P. Danielsson.  Euclidean distance mapping , 1980 .

[36]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[37]  Kazimierz Duzinkiewicz,et al.  Data-driven models for fault detection using kernel PCA: A water distribution system case study , 2012, Int. J. Appl. Math. Comput. Sci..

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

[39]  Manabu Kano,et al.  Statistical process monitoring based on dissimilarity of process data , 2002 .

[40]  Ron Wehrens,et al.  Principal Component Analysis , 2020, Use R!.

[41]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[42]  Steven X. Ding,et al.  Real-Time Implementation of Fault-Tolerant Control Systems With Performance Optimization , 2014, IEEE Transactions on Industrial Electronics.

[43]  Thomas J. McAvoy,et al.  Base control for the Tennessee Eastman problem , 1994 .

[44]  I. Jolliffe.  Principal Component Analysis , 2002 .

[45]  Zhiqiang Ge,et al.  Two-level multiblock statistical monitoring for plant-wide processes , 2009 .

[46]  Xue-feng Yan,et al.  Weighted kernel principal component analysis based on probability density estimation and moving window and its application in nonlinear chemical process monitoring , 2013 .

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

[48]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[49]  Age K. Smilde,et al.  Principal Component Analysis , 2003, Encyclopedia of Machine Learning.

[50]  Stelios Psarakis,et al.  Multivariate statistical process control charts: an overview , 2007, Qual. Reliab. Eng. Int..

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

[52]  Qing Yang,et al.  Online approach of fault diagnosis based on Lifting Wavelets and Moving Window PCA , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[53]  Xuefeng Yan,et al.  Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring , 2014 .

[54]  D. Seborg,et al.  Pattern Matching in Historical Data , 2002 .

[55]  Ali Cinar,et al.  Statistical process monitoring and disturbance diagnosis in multivariable continuous processes , 1996 .