Extensions to Regression Adjustment Techniques in Multivariate Statistical Process Monitoring
ARIZONA STATE UNIV TEMPE
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A common theme among the many existing multivariate statistical process monitoring MSPM methods is the recommendation that process knowledge be used to select a suitable monitoring procedure. Several methods possess the property of directional invariance, with shift detection performance depending only on the distance of a shift away from the target mean vector. This property is of special importance when characterizing a new process, or when available process knowledge suggests that shifts may occur in virtually any direction away from the target mean. In other cases, it is possible and may be desirable to increase a control schemes sensitivity by using knowledge of the process structure and possible upset mechanisms to aim the control procedure. This research identifies a potentially common MSPM scenario and extends the idea of using process knowledge to determine an appropriate control statistic for assignable cause detection and identification. Additionally, assumptions of normality and constant variance are imbedded in many statistical process monitoring procedures. For scenarios where monitoring with regression adjusted variables seems appropriate, but assumptions of normality and constant variance are violated, the use of prediction limits based on Generalized Linear Models theory was investigated and shown to be a potential improvement.
- Statistics and Probability
- Operations Research