Unevenly Sampled Dynamic Data Modeling and Monitoring With an Industrial Application

In this paper, a dynamic modeling method for unevenly sampled data is proposed for the monitoring of bi-layer (i.e., a process layer and a quality layer) dynamic processes. First, a novel uneven data dynamic canonical correlation analysis method with an integrated dynamic time window is proposed for interlayer latent structure modeling, which captures the dynamic relations between regularly sampled process data and quality data with slow and irregular sampling. The new model is a step toward big data modeling to deal with data irregularity and diversity. Second, after extracting covariations using an interlayer model, intralayer variations are extracted using subsequent principal component analysis on the residual subspaces of the original process data and quality data, respectively. Third, a concurrent monitoring method for unevenly sampled bi-layer data is proposed. Finally, the proposed method is demonstrated using an illustrative simulation example and applied successfully to a real blast furnace iron-making process.

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

[2]  S. Joe Qin,et al.  Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures , 2013 .

[3]  Zhou Qi,et al.  LGS-MMDPCA Based Modeling and Online Monitoring for Multiple Operating Batch Processes , 2016 .

[4]  Zhihuan Song,et al.  Autoregressive Dynamic Latent Variable Models for Process Monitoring , 2017, IEEE Transactions on Control Systems Technology.

[5]  Guang Wang,et al.  Quality-Related Fault Detection Approach Based on Orthogonal Signal Correction and Modified PLS , 2015, IEEE Transactions on Industrial Informatics.

[6]  Thomas J. McAvoy,et al.  Nonlinear FIR Modeling via a Neural Net PLS Approach , 1996 .

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

[8]  S. Joe Qin,et al.  Dynamic-Inner Partial Least Squares for Dynamic Data Modeling , 2015 .

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

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

[11]  Donghua Zhou,et al.  Diagnosis and Prognosis for Complicated Industrial Systems - Part I , 2016, IEEE Trans. Ind. Electron..

[12]  Riccardo Muradore,et al.  A PLS-Based Statistical Approach for Fault Detection and Isolation of Robotic Manipulators , 2012, IEEE Transactions on Industrial Electronics.

[13]  Sirish L. Shah,et al.  Modeling and control of multivariable processes: Dynamic PLS approach , 1997 .

[14]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[15]  Tianyou Chai,et al.  Fault Detection of Multimode Processes Using Concurrent Projection to Latent Structures , 2016 .

[16]  Chudong Tong,et al.  Decentralized Monitoring of Dynamic Processes Based on Dynamic Feature Selection and Informative Fault Pattern Dissimilarity , 2016, IEEE Transactions on Industrial Electronics.

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

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

[19]  Vincent Choqueuse,et al.  Diagnosis of Three-Phase Electrical Machines Using Multidimensional Demodulation Techniques , 2012, IEEE Transactions on Industrial Electronics.

[20]  Qiang Liu,et al.  Concurrent Canonical Correlation Analysis Modeling for Quality-Relevant Monitoring , 2016 .

[21]  Donghua Zhou,et al.  Diagnosis and Prognosis for Complicated Industrial Systems - Part II , 2016, IEEE Trans. Ind. Electron..

[22]  Youxian Sun,et al.  Multispace Total Projection to Latent Structures and its Application to Online Process Monitoring , 2014, IEEE Transactions on Control Systems Technology.

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

[24]  Kai-xiang Peng,et al.  Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application , 2013 .

[25]  W. Harmon Ray,et al.  Dynamic PLS modelling for process control , 1993 .

[26]  Barry M. Wise,et al.  A Theoretical Basis for the use of Principal Component Models for Monitoring Multivariate Processes , 1990 .

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

[28]  Si-Zhao Joe Qin,et al.  Survey on data-driven industrial process monitoring and diagnosis , 2012, Annu. Rev. Control..

[29]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[30]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[31]  S. Joe Qin,et al.  Reconstruction-Based Fault Identification Using a Combined Index , 2001 .

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

[33]  Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework , 2016, IEEE Transactions on Industrial Informatics.

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

[35]  Shen Yin,et al.  Velocity-Free Fault-Tolerant and Uncertainty Attenuation Control for a Class of Nonlinear Systems , 2016, IEEE Transactions on Industrial Electronics.

[36]  Donghua Zhou,et al.  Geometric properties of partial least squares for process monitoring , 2010, Autom..

[37]  John F. MacGregor.  STATISTICAL PROCESS CONTROL OF MULTIVARIATE PROCESSES , 1994 .

[38]  Catherine Porte,et al.  Automation and optimization of glycine synthesis , 1996 .

[39]  Edwin Lughofer,et al.  Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations , 2014, Inf. Fusion.

[40]  Donghua Zhou,et al.  Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach , 2011, IEEE Transactions on Neural Networks.