A Max-EWMA approach to monitor and diagnose faults of multivariate quality control processes

A new approach is developed in this paper to detect general mean shifts of multivariate quality control systems and to determine the quality characteristic(s) responsible for the shift. This approach takes advantage of both a decomposition method and an EWMA-based control statistics that are employed for multivariate normal distributions. In order to evaluate the performance of the proposed methodology, simulation studies are provided to estimate the in- and out-of-control average run lengths under different mean and variance shift scenarios. Simulation experiments are also given to compare the performances of the proposed procedure with the ones of the well-known MEWMA and MCUSUM methods. The results of the simulation studies show that while the proposed method is compatible to the other compared methods, it has a good ability of detecting the correct out-of-control characteristic(s) most of the times. At the end, a case study is provided to demonstrate the applicability of the proposed procedure.