Robust Monitoring of Contaminated Multivariate Data

Abstract EN

Monitoring a process that suffers from data contamination using a traditional multivariate T2 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 T2 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.