Algorithm for Correlation Diagnosis in Multivariate Process Quality Based on Optimal Typical Correlated Component Pairs Group

Abstract

Abstract Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new approach based on the optimal typical correlated component pairs group (OTCCPG) is proposed. Firstly, theorem of correlation decomposition is proved to decompose the correlation of all the quality components as serial correlations of component pairs, and then according to the transitivity of correlations of component pairs, the decomposition result is represented by a correlation set of typical correlated component pairs. Finally, algorithm of OTCCPG based on the maximum correlation spanning tree (MCST) is proposed, and T2 control charts to monitor the correlations of component pairs in OTCCPG are established to form the correlation diagnosis system. Theoretical analysis and practice prove that the proposed method could reduce the space complexity of the diagnosis system greatly.