Abstract
In this paper, multivariate statistical process monitoring approaches for key performance indicator (KPI) related static processes are reviewed under a unified framework. Based on their key nature in extracting KPI-related information from process variable space for performance monitoring, those approaches are analyzed and sorted into three categories: direct cross-correlation based decomposition method, modified least square regression based approaches, partial least square based approaches. In addition, their numerical properties and monitoring performance are compared in details. Finally the well-accepted TE benchmark process is utilized to demonstrate the theoretical comparison results and their monitoring performance from industrial viewpoint.
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