An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts

With modern data acquisition system and on-line computer used during production, it is now common to monitor several correlated quality variables simultaneously. Various multivariate control charts (e.g., Hotelling's T2, multivariate cumulative sum, and multivariate exponentially weighted moving average charts) have been designed for detecting mean shifts. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal. Using decision tree learning techniques, this work proposes a straightforward and effective model to detect the mean shifts in multivariate control charts. Experimental results using simulation show that the proposed model cannot only efficiently detect the mean shifts but also accurately identify the variables that have deviated from their original means. The shift direction of each of the deviated variables can also be determined in the meantime. The experimental results also indicate that the learning speed of the proposed decision tree learning-based model is much faster (25 times in this research) than that of a neural network-based model (another machine learning-based approach) for detecting mean shifts in multivariate control charts. The feature of fast learning makes the proposed DT learning-based model more adaptable to a dynamic process-monitoring scenario, in which constant model re-designing and re-learning is required. A bivariate case of the proposed multivariate model is presented. A demonstrative application is provided to illustrate the usage of the proposed decision tree learning-based approach to multivariate quality control.