Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window

A new adaptive kernel principal component analysis (KPCA) algorithm for monitoring nonlinear time-delay process is proposed. The main contribution of the proposed algorithm is to combine adaptive KPCA with moving window principal component analysis (MWPCA) algorithm, and exponentially weighted principal component analysis (EWPCA) algorithm respectively. The new algorithm prejudges the new available sample with MKPCA method to decide whether the model is updated. Then update the KPCA model using EWKPCA method. And also extend MPCA and EWPCA from linear data space to nonlinear data space effectively. Monitoring experiment is performed using the proposed algorithm. The simulation results show that the proposed method is effective.

[1]  Jyh-Cheng Jeng,et al.  Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms , 2010 .

[2]  Zhi-huan Song,et al.  Online monitoring of nonlinear multiple mode processes based on adaptive local model approach , 2008 .

[3]  Sigurd Skogestad,et al.  A New Class of Model-Based Static Estimators , 2013 .

[4]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[5]  Jie Zhang,et al.  Multiway Interval Partial Least Squares for Batch Process Performance Monitoring , 2013 .

[6]  Shuai Li,et al.  Dynamic processes monitoring using recursive kernel principal component analysis , 2012 .

[7]  Renquan Lu,et al.  Fault Identification of Nonlinear Processes , 2013 .

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

[9]  Jie Yu.  A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes , 2012 .

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

[11]  J. Romagnoli,et al.  A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring , 2006 .

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

[13]  Yi Cao,et al.  Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations , 2009 .

[14]  Ivan Prebil,et al.  Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method , 2011 .

[15]  Baisheng Chen,et al.  Development of variable pathlength UV-vis spectroscopy combined with partial-least-squares regression for wastewater chemical oxygen demand (COD) monitoring. , 2014, Talanta.

[16]  Ligang Wu,et al.  Sliding mode control with bounded L2 gain performance of Markovian jump singular time-delay systems , 2012, Autom..

[17]  Chai Tianyou.  Modeling and Simulation for Strip Tension in Continuous Annealing Process , 2007 .

[18]  Xiaozhan Yang,et al.  Dissipativity Analysis and Synthesis for Discrete-Time T–S Fuzzy Stochastic SystemsWith Time-Varying Delay , 2014, IEEE Transactions on Fuzzy Systems.

[19]  Wei Xing Zheng,et al.  Weighted H∞ model reduction for linear switched systems with time-varying delay , 2009, Autom..

[20]  Dae Sung Lee,et al.  Multivariate Online Monitoring of a Full-Scale Biological Anaerobic Filter Process Using Kernel-Based Algorithms. , 2006 .

[21]  Johan A. K. Suykens,et al.  Efficiently updating and tracking the dominant kernel principal components , 2007, Neural Networks.

[22]  Miki Haseyama,et al.  Adaptive example-based super-resolution using kernel PCA with a novel classification approach , 2011, EURASIP J. Adv. Signal Process..

[23]  Ligang Wu,et al.  A New Approach to Stability Analysis and Stabilization of Discrete-Time T-S Fuzzy Time-Varying Delay Systems , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[24]  Xiao Bin He,et al.  Variable MWPCA for Adaptive Process Monitoring , 2008 .

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

[26]  Zhiming Li,et al.  Modeling and Monitoring of Dynamic Processes , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[27]  J. Golinval,et al.  Fault detection based on Kernel Principal Component Analysis , 2010 .

[28]  A. J. Morris,et al.  Application of exponentially weighted principal component analysis for the monitoring of a polymer film manufacturing process , 2003 .

[29]  Jin Wen,et al.  A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform , 2014 .

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

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

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

[33]  R. Ocampo-Pérez,et al.  Adsorption of Fluoride from Water Solution on Bone Char , 2007 .

[34]  George W. Irwin,et al.  Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description , 2011 .

[35]  Mudassir Rashid,et al.  Nonlinear and Non-Gaussian Dynamic Batch Process Monitoring Using a New Multiway Kernel Independent Component Analysis and Multidimensional Mutual Information Based Dissimilarity Approach , 2012 .

[36]  Tianyou Chai,et al.  Fault diagnosis of continuous annealing processes using a reconstruction-based method , 2012 .

[37]  David Zhang,et al.  Two-stage image denoising by principal component analysis with local pixel grouping , 2010, Pattern Recognit..

[38]  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 .

[39]  Ali Ajami,et al.  Data driven approach for fault detection and diagnosis of turbine in thermal power plant using Independent Component Analysis (ICA) , 2012 .