Plant-wide performance monitoring has generated a lot of interest in the control engineering community. The idea is to judge the performance of a plant as a whole rather than looking at performance of individual controllers. Data based methods are currently used to generate a variety of statistical performance indices to help us judge the performance of production units and control assets. However, so much information can often be overwhelming if it lacks precise information. Powerful computing and data storage capabilities have enabled industries to store huge amounts of data. Commercial performance monitoring softwares such as those available from many vendor companies such as Honeywell, Matrikon, ExperTune etc typically use this data to generate huge amounts of information. The problem of data overload has in this way turned into an information overload problem. This work focuses on developing methods that reconcile these various statistical measures of performance and generate useful diagnostic measures in order to optimize process performance of a unit/plant. These methods are also able to identify the relative importance of controllers in the way that they affect the performance of the unit/plant under consideration.
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