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

Summary: [This abstract is based on the authors' abstract.] The increasing availability of high-volume, high-velocity data sets, often containing variables of dfferent 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 monitoring tools is vast, the application of these tools for big data sets has received less attention. In this expository paper, we give an overview of the current state of data-driven multivariate statistical process monitoring 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 paper 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 using Wikipedia search information and illustrate a few of the complexities of applying the available methods to a high-dimensional monitoring scenario. Throughout, we o↵er advice to practitioners and some suggestions for future research in this emerging area of research.

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  • Topics: Statistical Process Control (SPC)
  • Keywords: Control charts, Ensembles, Neural networks, Regression, Support vector machines, Variable selection
  • Author: Weese, Maria; Martinez, Waldyn; Megahed, Fadel M.; Jones-Farmer, L. Allison;
  • Journal: Journal of Quality Technology