Multivariate statistical process control with artificial contrasts

Wook‐Yeon Hwang · George C. Runger · Eugene Tuv

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Advanced Statistical Process Monitoring Statistical Methods and Applications Fault Detection and Control Systems

Abstract

A multivariate control region can be considered to be a pattern that represents the normal operating conditions of a process. Reference data can then be generated and used to learn the difference between this region and random noise. Then multivariate statistical process control can be converted to a supervised learning task. This can dramatically reshape the control region and open the control problem to a rich collection of supervised learning tools. Such tools provide generalization error estimates that can be used to specify error rates. The effectiveness of such an approach is shown here. Such a computational approach is now easily accomplished with modern computing resources. Examples use random forests and a regularized least squares classifier as the learners.

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1947Multivariate Quality Control-illustrated by the air testing of sample bombsights

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2015Statistical Perspectives on “Big Data”2021Transparent Sequential Learning for Statistical Process Control of Serially Correlated Data 2013Gower distance-based multivariate control charts for a mixture of continuous and categorical variables 2020Nonparametric monitoring of multivariate data via KNN learning 2015A general framework for monitoring complex processes with both in-control and out-of-control information 2013Improved design of kernel distance–based charts using support vector methods 2006Supervised learning for change-point detection 2010Integration of support vector machines and control charts for multivariate process monitoring

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