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Extensions to Regression Adjustment Techniques in Multivariate StatisticalProcess Monitoring

机译：多元统计过程监测中回归调整技术的扩展

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A common theme among the many existing multivariate statistical processmonitoring (MSPM) methods is the recommendation that process knowledge be used to select a suitable monitoring procedure. 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. 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.

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