Distribution-Free Adaptive Step-Down Procedure for Fault Identification

Abstract

Identifying the faulty variables of the out-of-control signal in high-dimensional process is an important problem for quality control areas. Even though there have been several procedures for fault variable identifications, most of the existing approaches assume the multivariate normal distribution of observations and are sensitive to the correlations between variables. Therefore, in this paper, we propose a new fault variable identification method that does not assume any specific distribution of observations. The proposed procedure based on one class classification method identifies the changed variables by identifying unchanged variables at each step using the information obtained from the previous steps. This strategy can reduce computational times when a few variables are changed in a high-dimensional process. In addition, the proposed procedure is robust to the correlations between variables, resulting in stable performance regardless of the number of changed variables. The experiment results with diverse dataset demonstrate superiority of the proposed distribution-free procedure.