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Use of Autoassociative Neural Networks for Signal Validation

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Abstract

Recently, the use of Autoassociative Neural Networks (AANNs) to perform on-line calibration monitoring of process sensors has been shown to not only be feasible, but practical as well. 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% 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.

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Authors and Affiliations

  1. Nuclear Engineering Department, The University of Tennessee, Knoxville, Tennessee, 37996, USA

    J. Wesley Hines & Robert E. Uhrig

  2. Systems Health Management Group, Honeywell Technology Center, Minneapolis, Minnesota, 55418, USA

    Darryl J. Wrest

Authors
  1. J. Wesley Hines
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  2. Robert E. Uhrig
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  3. Darryl J. Wrest
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Hines, J.W., Uhrig, R.E. & Wrest, D.J. Use of Autoassociative Neural Networks for Signal Validation. Journal of Intelligent and Robotic Systems 21, 143–154 (1998). https://doi.org/10.1023/A:1007981322574

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