Heuristic, systematic, and informational regularization for process monitoring

Abstract

Most data-based predictive modeling techniques have an inherent weakness in that they might give unstable or inconsistent results when the predictor data is highly correlated. Predictive modeling problems of this design are usually under-constrained and are termed ill-posed. This article presents several examples of ill-posed surveillance and diagnostic problems and regularization methods necessary for getting accurate and consistent prediction results. The examples include plant-wide sensor calibration monitoring and the inferential sensing of nuclear power plant feedwater flow using the following techniques: neural networks, nonlinear partial least squares techniques, linear regularization techniques implementing ridge regression, and informational complexity measures. © 2002 Wiley Periodicals, Inc.