Online Sensor Calibration Monitoring Uncertainty Estimation

Abstract Empirical modeling techniques have been applied to online process monitoring to detect equipment and instrumentation degradations. However, few applications provide prediction uncertainty estimates, which can provide a measure of confidence in decisions. This paper presents the development of analytical prediction interval estimation methods for three common nonlinear empirical modeling strategies: artificial neural networks, neural network partial least squares, and local polynomial regression. The techniques are applied to nuclear power plant operational data for sensor calibration monitoring, and the prediction intervals are verified via bootstrap simulation studies.

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