A boosting approach for understanding out-of-control signals in multivariate control charts

Esteban Alfaro · José Luis Alfaro Navarro · Matías Gámez · Noelia García

19 citations0/0 files storedInternational Journal of Production Research

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

Abstract

The most widely used tools in statistical quality control are control charts. However, the main problem of multivariate control charts, including Hotelling's T 2 control chart, lies in that they indicate that a change in the process has happened, but do not show which variable or variables are the source of this shift. Although a number of methods have been proposed in the literature for tackling this problem, the most usual approach consists of decomposing the T 2 statistic. In this paper, we propose an alternative method interpreting this task as a classification problem and solving it through the application of boosting with classification trees. The classifier is then used to determine which variable or variables caused the change in the process. The results prove this method to be a powerful tool for interpreting multivariate control charts.

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References

1992Multivariate Control Charts for Individual Observations 2002Multivariate Statistical Process Control with Industrial Applications 1995Decomposition of T 2 for Multivariate Control Chart Interpretation 1997A Practical Approach for Interpreting Multivariate T2 Control Chart Signals 1997A neural network approach for the analysis of control chart patterns 1998A review of neural networks for statistical process control 1995A multi-layer neural network model for detecting changes in the process mean 1994Identification and Quantification in Multivariate Quality Control Problems

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2017Monitoring multivariate process variability via eigenvalues 2015Diagnosing the variance shifts signal in multivariate process control using ensemble classifiers 2014Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control 2020Using Random Forest to Interpret Out-of-Control Signals 2012Identifying the out-of-control variables of multivariate control chart using ensemble SVM classifiers

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