Applying Decision Tree-Based Ensemble Classifiers for Diagnosing Mean Shift Signals in Multivariate Control Chart

Abstract

The Hotelling T^2 control chart is an important tool for monitoring process shift in multivariate statistical process control (MSPC). Detecting and diagnosing out-of-control variables are required tasks when a multivariate control chart signals. This paper presents a decision tree-based ensemble model to address diagnosing issue in multivariate process control. The commonly used ensemble methods, including bagging and AdaBoost are considered in this paper. To improve the classification performance, we propose using a set of features extracting from process data. Results from comparative studies indicate that these features with certain ensemble classifiers can significantly improve classification performance. The proposed approach contributes to process monitoring and identifyingmean shift sources in MSPC, which can assist engineers to effectively identifyresponsible variables and accelerate improvement action generation.