Robust monitoring of contaminated multivariate data

Abstract

Monitoring a process that suffers from data contamination using a traditional multivariate T 2 chart can lead to an excessive number of false alarms. A diagnostic statistic can be used to distinguish between real control chart signals due to assignable causes and signals due to contamination from a single outlier. In phase II analysis, a traditional T 2 control chart augmented by a diagnostic statistic improves the work stoppage rates for multivariate processes suffering from contaminated data and maintains the ability to detect process shifts. © 2013 Eric B. Howington.