Accession Number : ADA329727
Title : Extensions to Regression Adjustment Techniques in Multivariate Statistical Process Monitoring
Descriptive Note : Doctoral thesis
Corporate Author : ARIZONA STATE UNIV TEMPE
Personal Author(s) : Hauck, Daryl J.
PDF Url : ADA329727
Report Date : 26 SEP 1997
Pagination or Media Count : 158
Abstract : 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 scheme's 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.
Descriptors : *MULTIVARIATE ANALYSIS, *REGRESSION ANALYSIS, MATHEMATICAL MODELS, AUTOCORRELATION, ANALYSIS OF VARIANCE, LEAST SQUARES METHOD, CONTROL THEORY, STATISTICAL PROCESSES, CROSS CORRELATION.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE