Process and equipment monitoring methodologies applied to sensor calibration monitoring

Traditionally, the calibration of safety critical nuclear instrumentation has been performed at each refueling cycle. However, many nuclear plants have moved toward condition‐directed rather than time‐directed calibration. This condition‐directed calibration is accomplished through the use of on‐line monitoring. On‐line monitoring (OLM) uses an autoassociative empirical modeling architecture to assess instrument channel performance. An autoassociative architecture predicts a group of correct sensor values when supplied a group of sensor values that is usually corrupted with process and instrument noise, and could also contain faults such as sensor drift or complete failure. The Process and Equipment Monitoring (PEM) Toolbox, which was developed at The University of Tennessee, is a set of MATLAB based tools, which have been developed to support the design of process and equipment condition monitoring systems. Its purpose is to provide the necessary functionality so that different empirical modeling and uncertainty estimation methods may be investigated and compared. This paper discusses the purpose and architecture of the PEM Toolbox, as well as presenting an example application to power plant sensor calibration monitoring using actual plant data.