Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data

References

• Arteaga, F. and Ferrer, A. (2002). “Dealing with Missing Data in MSPC: Several Methods, Different Interpretations, Some Examples”. Journal of Chemometrics 16 (8–10), pp. 408–418.
   Web of Science ®Google Scholar
• Barceló, S.; Vidal-Puig, S.; and Ferrer, A. (2010). “Comparison of Multivariate Statistical Methods for Dynamic Systems Modeling”. Quality & Reliability Engineering International 27 (1), pp. 107–124.
   Web of Science ®Google Scholar
• Bersimis, S.; Psarakis, S.; and Panaretos, J. (2006). “Multivariate Statistical Process Control Charts: An Overview”. Quality & Reliability Engineering International 23, pp. 517–543.
   Web of Science ®Google Scholar
• Bisgaard, S. (2012). “The Future of Quality Technology: From a Manufacturing to a Knowledge Economy & From Defects to Innovations”. Quality Engineering 24, pp. 30–36.
   Web of Science ®Google Scholar
• Chiang, L. and Colegrove, L. (2007). “Industrial Implementation of On-Line Multivariate Quality Control”. Chemometrics and Intelligent Laboratory Systems 88, pp. 143–153.
   Web of Science ®Google Scholar
• Chiang, L.; Russell, E.; and Braatz, R. (2001). Fault Detection and Diagnosis in Industrial Systems. London, UK: Springer-Verlag.
   Google Scholar
• Choi, S.; Martin, E.; Morris, A.; and Lee, I. (2006). “Adaptive Multivariate Statistical Process Control for Monitoring Time-Varying Processes”. Industrial & Engineering Chemistry Research 45, pp. 3108–3118.
   Web of Science ®Google Scholar
• Dayal, B. S. and MacGregor, J. F. (1997a). “Improved PLS Algorithms”. Journal of Chemometrics 11 (1), pp. 73–85.
   Web of Science ®Google Scholar
• Dayal, B. S. and MacGregor, J. F. (1997b). “Recursive Exponentially Weighted PLS and Its Applications to Adaptive Control and Prediction”. Journal of Process Control 7 (3), pp. 169–179.
   Web of Science ®Google Scholar
• Elshenawy, L. and Awad, H. (2012). “Recursive Fault Detection and Isolation Approaches of Time-Varying Processes”. Industrial & Engineering Chemistry Research 51 (29), pp. 9812–9824.
   Web of Science ®Google Scholar
• Epprecht, E. K.; Barbosa, L. F. M.; and Simões, B. F. T. (2011). “SPC of Multiple Stream Processes: A Chart for Enhanced Detection of Shifts in One Stream”. Production 21, pp. 242–253.
   Google Scholar
• Epprecht, E. K. and Simões, B. F. T. (2013). “Statistical Control of Multiple-Steam Processes: A Literature Review”. Paper presented at the 11th International Workshop on Intelligent Statistical Quality Control, Sydney, Australia.
   Google Scholar
• Gallagher, N.; Wise, B.; Butler, S.; White, D.; and Barna, G. (1997). “Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness Through Model Updating”. Process: Impact of Measurement Selection and Data Treatment on Sensitivity, Safe Process 1997, pp. 26–27.
   Google Scholar
• He, B. and Yang, X. (2011). “A Model Updating Approach of Multivariate Statistical Process Monitoring”. IEEE International Conference on Information and Automation (ICIA), pp. 400–405.
   Google Scholar
• He, X. and Yang, Y. (2008). “Variable MWPCA for Adaptive Process Monitoring”. Industrial & Engineering Chemistry Research 47 (2), pp. 419–427.
   Web of Science ®Google Scholar
• Hubert, M.; Rousseeuw, P.; and van den Branden, K. (2005). “ROBPCA: A New Approach to Robust Principal Components Analysis”. Technometrics 47, pp. 64–79.
   Web of Science ®Google Scholar
• Jackson, J. and Mudholkar, G. (1979). “Control Procedures for Residuals Associated with Principal Component Analysis”. Technometrics 21 (3), pp. 341–349.
   Web of Science ®Google Scholar
• Jeng, J.-C. (2010). “Adaptive Process Monitoring Using Efficient Recursive PCA and Moving Window PCA Algorithms”. Journal of the Taiwan Institute of Chemical Engineer 44, pp. 475–481.
   Web of Science ®Google Scholar
• Jensen, W.; Birch, J.; and Woodall, W. (2007). “High Breakdown Estimation Methods for Phase I Multivariate Control Charts”. Quality and Reliability Engineering International 23 (5), pp. 615–629.
   Web of Science ®Google Scholar
• Jin, H.; Lee, Y.; Lee, G.; and Han, C. (2006). “Robust Recursive Principal Component Analysis Modeling for Adaptive Monitoring”. Industrial & Engineering Chemistry Research 45 (20), pp. 696–703.
   Web of Science ®Google Scholar
• Jolliffe, I. (2002). Principal Component Analysis, 2nd edition. New York, NY: Springer.
   Google Scholar
• Jolliffe, I. T.; Trendafilov, N. T.; and Uddin, M. (2003). “A Modified Principal Component Technique Based on the LASSO”. Journal of Computational and Graphical Statistics 12, pp. 531–547.
   Web of Science ®Google Scholar
• Kourti, T. (2005). “Application of Latent Variable Methods to Process Control and Multivariate Statistical Process Control in Industry”. International Journal of Adaptive Control and Signal Processing 19 (4), pp. 213–246.
   Web of Science ®Google Scholar
• Kruger, U. and Xie, L. (2012). Advances in Statistical Monitoring of Complex Multivariate Processes: With Applications in Industrial Process Control. New York, NY: John Wiley.
   Google Scholar
• Kruger, U.; Zhou, Y.; and Irwin, G. (2004). “Improved Principal Component Monitoring of Large-Scale Processes”. Journal of Process Control 14 (8), pp. 879–888.
   Web of Science ®Google Scholar
• Ku, W.; Storer, R.; and Georgakis, C. (1995). “Disturbance Detection and Isolation by Dynamic Principal Component Analysis”. Chemometrics and Intelligent Laboratory Systems 30 (1), pp. 179–196.
   Web of Science ®Google Scholar
• Lee, J.; Qiu, H.; Yu, G.; Lin, J.; and Services, R. T. (2007). “Bearing Data Set”. IMS, University of Cincinnati. NASA Ames Prognostics Data Repository.
   Google Scholar
• Li, W.; Yue, H.; Valle-Cervantes, S.; and Qin, S. (2000). “Recursive PCA for Adaptive Process Monitoring”. Journal of Process Control 10 (5), pp. 471–486.
   Web of Science ®Google Scholar
• Luo, R.; Misra, M.; and Himmelblau, D. (1999). “Sensor Fault Detection via Multiscale Analysis and Dynamic PCA”. Industrial & Engineering Chemistry Research 38 (4), pp. 1489–1495.
   Web of Science ®Google Scholar
• Miller, P.; Swanson, R.; and Swanson, C. H. (1998). “Contribution Plots: A Missing Link in Multivariate Quality Control”. Applied Mathematics and Computer Science 8, pp. 775–792.
   Google Scholar
• Nomikos, P. and MacGregor, J. (1995). “Multivariate SPC Charts for Monitoring Batch Processes”. Technometrics 37, pp. 41–59.
   Web of Science ®Google Scholar
• Qin, J.; Valle-Cervantes, S.; and Piovoso, M. (2001). “On Unifying Multi-Block Analysis with Applications to Decentralized Process Monitoring”. Journal of Chemometrics 15, pp. 715–742.
   Web of Science ®Google Scholar
• Qin, S. (2003). “Statistical Process Monitoring: Basics and Beyond”. Journal of Chemometrics 17, pp. 480–502.
   Web of Science ®Google Scholar
• R Core Team (2014). “R: A Language and Environment for Statistical Computing”.
   Google Scholar
• Rato, T. and Reis, M. (2013a). “Advantage of Using Decorrelated Residuals in Dynamic Principal Component Analysis for Monitoring Large-Scale Systems”. Industrial & Engineering Chemistry Research 52 (38), pp. 13685–13698.
   Web of Science ®Google Scholar
• Rato, T. and Reis, M. (2013b). “Defining the Structure of DPCA Models and its Impact on Process Monitoring and Prediction Activities”. Chemometrics and Intelligent Laboratory Systems 125, pp. 74–86.
   Web of Science ®Google Scholar
• Rato, T. and Reis, M. (2013c). “Fault Detection in the Tennessee Eastman Benchmark Process Using Dynamic Principal Components Analysis Based on Decorrelated Residuals (DPCA-DR)”. Chemometrics and Intelligent Laboratory Systems 125, pp. 101–108.
   Web of Science ®Google Scholar
• Runger, G. C.; Alt, F. B.; and Montgomery, D. C. (1996). “Controlling Multiple Stream Processes with Principal Components”. International Journal of Production Research 34 (11), pp. 2991–2999.
   Web of Science ®Google Scholar
• Serneels, S. and Verdonck, T. (2008). “Principal Component Analysis for Data Containing Outliers and Missing Elements”. Computational Statistics & Data Analysis 52 (3), pp. 1712–1727.
   Web of Science ®Google Scholar
• Todorov, V. (2013). “Scalable Robust Estimators with High Breakdown Point for Incomplete Data”. R package, version 0.4–4.
   Google Scholar
• Todorov, V. and Filzmoser, P. (2009). “An Object-Oriented Framework for Robust Multivariate Analysis”. Journal of Statistical Software 32 (3), pp. 1–47.
   Web of Science ®Google Scholar
• Treasure, R. J.; Kruger, U.; and Cooper, J. E. (2004). “Dynamic Multivariate Statistical Process Control Using Subspace Identification”. Journal of Process Control 14 (3), pp. 279–292.
   Web of Science ®Google Scholar
• Tsung, F. (2000). “Statistical Monitoring and Diagnosis of Automatic Controlled Processes Using Dynamic PCA”. International Journal of Production Research 38 (3), pp. 625–637.
   Web of Science ®Google Scholar
• Valle, S.; Li, W.; and Qin, S. (1999). “Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods”. Industrial & Engineering Chemistry Research 38 (11), pp. 4389–4401.
   Web of Science ®Google Scholar
• Verboven, S. and Hubert, M. (2005). “LIBRA: A MATLAB Library for Robust Analysis”. Chemometrics and Intelligent Laboratory Systems 75, pp. 127–136.
   Web of Science ®Google Scholar
• Walczak, B. and Massart, D. (2001). “Dealing with Missing Data. Part I”. Chemometrics & Intelligent Laboratory Systems 58, pp. 15–27.
   Web of Science ®Google Scholar
• Wang, X.; Kruger, U.; and Irwin, G. (2005). “Process Monitoring Approach Using Fast Moving Window PCA”. Industrial & Engineering Chemistry Research 44 (15), pp. 5691–5702.
   Web of Science ®Google Scholar
• Wold, S. (1994). “Exponentially Weighted Moving Principal Components Analysis and Projections to Latent Structures”. Chemometrics and Intelligent Laboratory Systems 23 (1), pp. 149–161.
   Web of Science ®Google Scholar
• Woodall, W. and Montgomery, D. (2014). “Some Current Directions in the Theory and Application of Statistical Process Monitoring”. Journal of Quality Technology 46 (1), pp. 78–94.
   Web of Science ®Google Scholar
• Zou, H.; Hastie, T.; and Tibshirani, R. (2006). “Sparse Principal Component Analysis”. Journal of Computational and Graphical Statistics 15, pp. 265–286.
   Web of Science ®Google Scholar