Abstract
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.
Highlights
Davis and Adams [1] consider the problem of dealing with contaminated data in univariate control charts
We propose a secondary diagnostic statistic (DS) that is calculated after the T2 chart signals
Phase II monitoring of multivariate process data in the presence of occasional data contamination using the T2 chart can be effectively augmented by a secondary diagnostic statistic
Summary
Davis and Adams [1] consider the problem of dealing with contaminated data in univariate control charts They consider monitoring a process for which measurement systems are problematic or unreliable, leading to occasional unusual measurements for key quality characteristics. These atypical measurements do not reflect the true state of the process and are referred to as outliers. If the value of the diagnostic statistic does not exceed the threshold, the signal is interpreted as a process problem and appropriate action is recommended The benefit of such a scheme is clearoccurrence of unwarranted work stoppage is reduced and detrimental process adjustments are avoided. We propose an extension of the diagnostic statistic technique for use in conjunction with phase II multivariate process monitoring via the Hotelling T2 chart
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