Clustering algorithm-based control charts

Abstract

Hotelling's T2 control chart is widely used as a representative method to efficiently monitor multivariate processes. However, they have some parametric restrictions that may not be applicable for modern manufacturing systems complicated. In the present study we propose a clustering algorithm-based control chart that overcomes the limitation posed by the parametric assumption in existing control chart methods. The simulation results showed that the proposed clustering algorithm-based control charts outperformed Hotelling's T2 control charts especially when process data follow the nonnormal distributions.