A Simple Variable Screening Method for the Mahalanobis Taguchi Method

In this study, we describe a new variable selection method for the Mahalanobis Taguchi (MT) method. Typically, an orthogonal array is used for variable selection in the MT method. However, in this study, we developed a more intuitive and simpler variable selection method using a screening approach for identification of appropriate variables for diagnosis prior to creating the Mahalanobis space, in contrast to the conventional method, in which selection of the variables was carried out by repeated updating of the Mahalanobis space. Thus, the calculation in the proposed method was simpler than that in the conventional approach. We confirmed the effectiveness of the proposed method using a two-class diagnosis method (normal and abnormal). The results suggested that the proposed method had better diagnostic accuracy and robustness for changing supervised data.