System Monitoring with Real-Time Contrasts

References

• Apley, D. and Shi, J. (1999). “The GLRT for Statistical Process Control of Autocorrelated Processes”. IIE Transactions 31 (12), pp. 1123–1134.
   Web of Science ®Google Scholar
• Apley, D. and Shi, J. (2001). “A Factor-Analysis Method for Diagnosing Variability in Mulitvariate Manufacturing Processes”. Technometrics 43 (1), pp. 84–95.
   Web of Science ®Google Scholar
• Borror, C.; Montgomery, D.; and Runger, G. (1999). “Robustness of the EWMA Control Chart to Non-Normality”. Journal of Quality Technology 31 (3), pp. 309–316.
   Web of Science ®Google Scholar
• Breiman, L.; Friedman, J.; Olshen, R.; and Stone, C. (1984). Classification and Regression Trees. Belmont, MA: Wadsworth.
   Google Scholar
• Breiman, L. (2001). “Random Forests”. Machine Learning 45 (1), pp. 5–32.
   Web of Science ®Google Scholar
• Byon, E.; Shrivastava, A.; and Ding, Y. (2010). “A Classification Procedure for Highly Imbalanced Class Sizes”. IIE Transactions 42 (4), pp. 288–303.
   Web of Science ®Google Scholar
• Camci, F.; Chinnam, R.; and Ellis, R. (2008). “Robust Kernel Distance Multivariate Control Chart Using Support Vector Principles”. International Journal of Production Research 46 (18), pp. 5075–5095.
   Web of Science ®Google Scholar
• Chinnam, R. (2002). “Support Vector Machines for Recognizing Shifts in Correlated and Other Manufacturing Processes”. International Journal of Production Research 40 (17), pp. 4449–4466.
   Web of Science ®Google Scholar
• Chou, Y.; Polansky, A.; and Mason, R. (1998). “Transforming Non-Normal Data to Normality in Statistical Process Control”. Journal of Quality Technology 30 (2), pp. 133–141.
   Web of Science ®Google Scholar
• Cook, D. and Chiu, C. (1998). “Using Radial Basis Function Neural Networks to Recognize Shifts in Correlated Manufacturing Process Parameters”. IIE Transactions 30 (3), pp. 227–234.
   Web of Science ®Google Scholar
• Ding, Y.; Zeng, L.; and Zhou, S. (2006). “Phase I Analysis for Monitoring Nonlinear Profiles in Manufacturing Processes”. Journal of Quality Technology 38 (3), pp. 199–216.
   Web of Science ®Google Scholar
• Hawkins, D. and Deng, Q. (2009). “Combined Charts for Mean and Variance Information”. Journal of Quality Technology, 41 (4), pp. 415–425.
   Web of Science ®Google Scholar
• Hettich, S. and Bay, S. (1999). “The UCI KDD Archive” [http://kdd.ics.uci.edu].
   Google Scholar
• Hotelling, H. (1947). “Multivariate Quality Control-Illustrated by the Air Testing of Sample Bombsights”. In Eisenhart, C.; Hastay, M.; and Wallis, W., editors, Techniques of Statistical Analysis, pp. 111–184. New York, NY: McGraw-Hill.
   Google Scholar
• Hu, J. and Runger, G. C. (2010). “Time-Based Detection of Changes to Multivariate Patterns”. Annals of Operations Research 174 (1), pp. 67–81.
   Web of Science ®Google Scholar
• Hu, J.; Runger, G. C.; and Tuv, E. (2007). “Tuned Artificial Contrasts to Detect Signals”. International Journal of Production Research 45 (23), pp. 5527–5534.
   Web of Science ®Google Scholar
• Hwang, W.; Runger, G. C.; and Tuv, E. (2007). “Multivariate Statistical Process Control with Artificial Contrasts”. IIE Transactions 39, pp. 659–669.
   Web of Science ®Google Scholar
• Imtiaz, S. and Shah, S. (2008). “Treatment of Missing Values in Process Data Analysis”. The Canadian Journal of Chemical Engineering 86 (5), pp. 838–858.
   Web of Science ®Google Scholar
• Kim, S.; Sukchotrat, T.; and Park, S. (2011). “A Nonparametric Fault Isolation Approach Through One-Class Classification Algorithms”. IIE Transactions, 43 (7), pp. 505–517.
   Web of Science ®Google Scholar
• Li, F.; Runger, G. C.; and Tuv, E. (2006). “Supervised Learning for Change-Point Detection”. International Journal of Production Research 44 (14), pp. 2853–2868.
   Web of Science ®Google Scholar
• Li, J.; Jin, J.; and Shi, J. (2008). “Causation-Based T2 Decomposition for Multivariate Process Monitoring and Diagnosis”. Journal of Quality Technology 40 (1), pp. 46–58.
   Web of Science ®Google Scholar
• Lowry, C. A.; Woodall, W. H.; Champ, C. W.; and Rigdon, S. E. (1992). “A Multivariate Exponentially Weighted Moving Average Chart”. Technometrics 34, pp. 46–53.
   Web of Science ®Google Scholar
• Morrison, D. (1967). Multivariate Statistical Methods. New York, NY: McGraw-Hill.
   Google Scholar
• Perlich, C.; Provost, F.; and Simonoff, J. S. (2003). “Tree Induction vs. Logistic Regression: A Learning-Curve Analysis”. Journal of Machine Learning Research 4, pp. 211–255.
   Web of Science ®Google Scholar
• Prabhu, A.; Edgar, T.; and Good, R. (2009). “Missing Data Estimation for Run-To-Run EWMA-Controlled Processes”. Computers # Chemical Engineering 33 (11), pp. 1861–1869.
   Web of Science ®Google Scholar
• Qiu, P. (2008). “Distribution-Free Multivariate Process Control Based on Log-Linear Modeling”. IIE Transactions 40 (7), pp. 664–677.
   Web of Science ®Google Scholar
• R Development Core Team. R: A Language and Environment for Statistical Com- puting. R Foundation for Statistical Computing, Vienna, Austria, 2009. URL http://www.R-project.org. ISBN 3-900051-07-0.
   Google Scholar
• Runger, G.; Alt, F.; and Montgomery, D. (1996). “Contributors to A Multivariate Statistical Process Control Chart Signal”. Communications in Statistics–Theory and Methods 25 (10), pp. 2203–2213.
   Web of Science ®Google Scholar
• Runger, G. C. (1996). “Projections and the U2 Multivariate Control Chart”. Journal of Quality Technology 28, pp. 313–319.
   Web of Science ®Google Scholar
• Runger, G. C. (2002). “Assignable Causes and Autocorrelation: Control Charts on Observations or Residuals?”. Journal of Quality Technology 34 (2), pp. 165–170.
   Web of Science ®Google Scholar
• Runger, G. C. and Testik, M. C. (2004). “Multivariate Extensions to Cumulative Sum Control Charts”. Quality and Reliability Engineering International 20 (4), pp. 387–396.
   Google Scholar
• Stoumbos, Z. and Sullivan, J. (2002). “Robustness to Non-Normality of the Multivariate EWMA Control Chart”. Journal of Quality Technology 34 (3), pp. 260–276.
   Web of Science ®Google Scholar
• Sukchotrat, T.; Kim, S.; and Tsung, F. (2010). “One-Class Classification-Based Control Charts for Multivariate Process Monitoring”. IIE Transactions 42 (2), pp. 107–120.
   Web of Science ®Google Scholar
• Sullivan, J. and Woodall, W. (2000). “Change-Point Detection of Mean Vector or Covariance Matrix Shifts Using Multivariate Individual Observations”. IIE Transactions 32 (6), pp. 537–549.
   Web of Science ®Google Scholar
• Sun, R. and Tsung, F. (2003). “A Kernel-Distance-Based Multivariate Control Chart Using Support Vector Methods”. International Journal of Production Research 41 (13), pp. 2975–2989.
   Web of Science ®Google Scholar
• Wang, K. and Tsung, F. (2007). “Run-to-Run Process Adjustment Using Categorical Observations”. Journal of Quality Technology 39 (4), pp. 312–325.
   Web of Science ®Google Scholar
• Wang, K. and Jiang, W. (2009). “High-Dimension Process Monitoring and Fault Isolation via Variable Selection”. Journal of Quality Technology 41 (3), pp. 247–258.
   Web of Science ®Google Scholar
• Zamba, K. D. and Hawkins, D. M. (2009). “A Multivariate Change-Point Model for Change in Mean Vector and/or Covariance Structure”. Journal of Quality Technology 41 (3), pp. 285–303.
   Web of Science ®Google Scholar
• Zou, C. and Tsung, F. (2010). “Likelihood Ratio-Based Distribution-Free EWMA Control Charts”. Journal of Quality Technology 42 (2), pp. 174–196.
   Web of Science ®Google Scholar
• Zou, C.; Tsung, F.; and Wang, Z. (2008). “Monitoring Profiles Based on Nonparametric Regression Methods”. Technometrics 50 (4), pp. 512–526.
   Web of Science ®Google Scholar