Use of Autoassociative Neural Networks for Signal Validation

Recently, the use of Autoassociative Neural Networks (AANNs) to perform on-line calibration monitoring of process sensors has been shown to be not only feasible but practical. This paper summarizes the results of applying AANNs to instrument surveillance and calibration monitoring at Florida Power Corporation’s Crystal River #3 Nuclear Power Plant and at the Oak Ridge National Laboratory High Flux Isotope Reactor. In both cases sensor drifts are detectable at a nominal level of 0.5% of the instrument’s full scale range. This paper will discuss the selection of a five layer neural network architecture, a robust training paradigm, the input selection criteria, and a retuning algorithm.