Statistical Process Monitoring

UT Arlington Faculty:Victoria Chen
Collaborators: Seoung Bum Kim, Kwok Tsui, Li Zeng, Clint Carter
Ph.D. Students:Terrence Murphy (Georgia Tech 2004), Thuntee Sukchotrat (2008), Panitarn Chongfuangprinya (2009), Weerawat Jitpitaklert (2009), Poovich Phaladiganon (2012), Smriti Neogi (2014), Kin Ming Kam (2014)

Funding: Williams-Pyro 2007, Luminant Power 2007-10
Topics: Hotelling’s T2 statistic, One-class classification, Supervised control charts.

Description: Manufacturing systems have hundreds of processes that require monitoring, and statistical process control is a well-known tool used for properly maintaining processes. However, real world processes often do not follow the assumptions underlying traditional methods, and many process are complex, involving multiple stages. The need for practical methods that can handle large and complex systems still exists. For multivariate monitoring, COSMOS has developed methods that combine data mining with statistical process control to enable efficient and effective monitoring of a large number of correlated process variables. For multi-stage processes, COSMOS has developed systematic methodologies through the integration of advanced statistical techniques in graphical models and engineering insights to the processes.

  • Phaladiganon, P., S. B. Kim, and V. C. P. Chen (2014).  “A Density-focused Support Vector Data Description Method.”  Quality and Reliability Engineering International, 30(6), pp. 879–890.  COSMOS Technical Report 12-11.
  • Kim, S. B., W. Jitpitaklert, V. C. P. Chen, J. Lee, and S.-K. Park  (2013).  “Data Mining Model Adjustment Control Charts for Cascade Processes.”  European Journal of Industrial Engineering, 7(4), pp. 442–455.  COSMOS Technical Report 10-07.
  • Phaladiganon, P., S. B. Kim, V. C. P. Chen, and W. Jiang (2013).  “Principal Component Analysis-Based Control Charts for Multivariate Nonnormal Distributions.”  Expert Systems with Applications, 40, pp. 3044–3054.  COSMOS Technical Report 12-02.
  • Murphy, T. E., Y. Lin, K.-L. Tsui, V. C. P. Chen, and J. K. Allen (2011).  “Identifying Feasible Design Concepts for Products with Competing Performance Requirements by Meta-Modeling of Loss-Scaled Principal Components.”  Quality Engineering, 23(2), pp. 167–179.
  • Phaladiganon, P., S. B. Kim, V. C. P. Chen, J.-G. Baek, and S.-K. Park (2011).  “Bootstrap-Based T2 Multivariate Control Charts.”  Communications in Statistics – Simulation and Computation, 40(5), pp. 645–662.  COSMOS Technical Report 10-01.
  • Sukchotrat, T., S. B. Kim, K.-L. Tsui, V. C. P. Chen (2011).  “Integration of Classification Algorithms and Control Chart Techniques for Monitoring Multivariate Processes.”  Journal of Statistical Computation and Simulation, 81(12), pp. 1897–1911.  COSMOS Technical Report 08-01.
  • Bush, H. M., P. Chongfuangprinya, V. C. P. Chen, T. Sukchotrat, and S. B. Kim (2010).  “Nonparametric Multivariate Control Charts Based on a Linkage Ranking Algorithm.”  Quality and Reliability Engineering International, 26(7), pp. 663–675.    COSMOS Technical Report 09-05.
  • Kim, S. B, W. Jitpitaklert, and T. Sukchotrat (2010). “One-Class Classification-Based Control Charts for Monitoring Autocorrelated Process.” Communications in Statistics – Simulation and Computation, 39(3), pp. 461–474.
  • Sukchotrat, T., S. B. Kim, F. Tsung (2010). “One-Class Classification Method for Phase I in Statistical Process Control.” IIE Transactions, 42(2), pp. 107–120. COSMOS Technical Report 08-03.
  • Kim, S. B., K.-L. Tsui, T. Sukchotrat, V. C. P. Chen (2009).  “A Comparison Study and Discussion of the Mahalanobis-Taguchi System.”  International Journal of Industrial and Systems Engineering, 4(6), pp. 631–644.
  • Sukchotrat, T., S. B. Kim, V. C. P. Chen, C. Carter, W. Dockery, and R. Tapia (2008). “An Application of Supervised Multivariate Control Charts on a Power Plant Data.” In Proceedings of the 2008 IE Research Conference, Vancouver, BC, Canada.
  • Zeng, L. and S. Zhou (2008). “Variability Monitoring of Multistage Manufacturing Processes using Regression Adjustment Methods.” IIE Transactions, 40(2), pp. 109-121.
  • Zeng, L., N. Jin, and S. Zhou (2008). “Multiple Fault Signature Integration and Enhancing for Variation Source Identification in Manufacturing Processes.” IIE Transactions, 40(10), pp. 919-930.
  • Zeng, L. and S. Zhou (2007). “Inferring the Interactions in Complex Manufacturing Processes using Graphical Models.” Technometrics, 49(4), pp. 373-381.
  • Ding, Y., L. Zeng, and S. Zhou (2006). “Phase I Analysis for Monitoring Nonlinear ProfileSignals in Manufacturing Processes.” Journal of Quality Technology, 38(3), pp. 199-216.