Sparse dynamic inner principal component analysis for process monitoring

In this paper, a novel sparse dynamic inner principal component analysis (SDiPCA) method is proposed for process monitoring. First, a simple regression-type approach of dynamic inner principal component analysis (DiPCA) is discussed. To derive sparse principal components, an elastic net regularization is imposed on this regression-type problem. Then a new optimization criterion is established and solved through an alternating algorithm. On the basis of the SDiPCA model, four monitoring statistics are constructed to reflect the process status. Also, the reconstruction-based contribution (RBC) method is employed to isolate faulty variables. Finally, a case study on the Tennessee Eastman process is conducted to illustrate the superior performance of the proposed SDiPCA method compared with DiPCA method.

[1]  R. Tibshirani,et al.  Sparse Principal Component Analysis , 2006 .

[2]  Nickolay T. Trendafilov,et al.  From simple structure to sparse components: a review , 2014, Comput. Stat..

[3]  Murat Kulahci,et al.  Selection of Non-zero Loadings in Sparse Principal Component Analysis , 2017 .

[4]  Richard D. Braatz,et al.  Perspectives on process monitoring of industrial systems , 2016, Annu. Rev. Control..

[5]  R. Tibshirani,et al.  A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. , 2009, Biostatistics.

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

[7]  Biao Huang,et al.  Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008-2017 , 2018, The Canadian Journal of Chemical Engineering.

[8]  Si-Zhao Joe Qin,et al.  Reconstruction-based contribution for process monitoring , 2009, Autom..

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

[10]  Murat Kulahci,et al.  Real-time fault detection and diagnosis using sparse principal component analysis , 2017, Journal of Process Control.

[11]  Chun-Hou Zheng,et al.  A Simple Review of Sparse Principal Components Analysis , 2016, ICIC.

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

[13]  S. Joe Qin,et al.  A novel dynamic PCA algorithm for dynamic data modeling and process monitoring , 2017 .

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

[15]  Rasmus Larsen,et al.  SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling , 2018 .

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

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