2015 QPRC

Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective

 
Maria Weese, Waldyn Martinez,
Department of Information Systems and Analytics
Miami University, Oxford, OH, USA
(weeseml@miamioh.edu martinwg@miamioh.edu)

Fadel M. Megahed
Department of Industrial and Systems Engineering
Auburn University, Auburn, AL, USA
(fmegahed@auburn.edu)

 
L. Allison Jones-Farmer
Department of Information Systems and Analytics
Miami University, Oxford, OH, USA
(farmerl2@miamioh.edu)

The increasing availability of high volume, high velocity data sets, often containing variables of different data types, brings an increasing need for monitoring tools that are designed to handle these big data sets.  While the research on multivariate statistical process control tools is vast, the application of these tools for big data sets has received less attention.  This discussion serves to give an overview of the current state of big data-driven multivariate statistical process control methodology. We highlight some of the main directions involving statistical learning and dimension reduction techniques applied to control charts in research from supply chain, engineering, computer science and statistics.  The goal of this discussion is to bring into better focus some of the monitoring and surveillance methodology informed by data-mining techniques that show promise for monitoring large and diverse data sets.  We introduce an example and illustrate a few of the complexities of applying the available methods to a high dimensional monitoring scenario.