A Learning-Based Decision Support Framework for the Automated Classification of Multivariate Control Chart Signals

Multivariate control charts (MCC) are widely used to detect out-of-control (OOC) situations in interrelated processes; however, they do not directly provide information about the source(s) of these signals. Although various methods for signal decomposition and interpretation have been proposed in the literature, most of them are limited to statistical interpretation and do not support automated signal classification. To overcome this limitation, an intelligent decision-support approach combining MCCs and machine learning for OOC signal classification has been developed. Interrelated cost and schedule performance indicators obtained through earned value analysis (EVA) were monitored by a Hotelling T2 control chart. When an OOC signal occurred, the associated variable(s) were identified using the Mason–Young–Tracy (MYT) decomposition method, and the resulting MYT classifications were used as class labels for supervised learning of an artificial neural network (ANN). The Box–Behnken experimental design was used to determine the optimal network architecture and training hyperparameters of the ANN. The findings showed that the optimized ANN model achieved 93.33% classification accuracy, and the optimization model explained 85.14% of the variation in Mean Squared Error (MSE). The main contribution of this study is the integration of statistical signal decomposition and machine learning into a learning-based decision-support mechanism for the automated interpretation of MCC signals. The developed approach provides a systematic, practical decision-support tool for identifying which EVA-based performance parameter(s) are associated with an OOC signal in the monitoring of complex processes.