Robust Monitoring of Contaminated Multivariate Data

摘要

Monitoring a process that suffers from data contamination using a traditional multivariateT2chart 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 traditionalT2control 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.