A novel approach to process modeling for instrument surveillance and calibration verification

This work presents an empirical modeling approach combining a bilinear modeling technique, partial least squares, with the universal function approximation abilities of single hidden layer nonlinear artificial neural networks. This approach, referred to as neural network partial least squares (NNPLS), is compared to the common autoassociative artificial neural network. The NNPLS model is embedded into a graphical user interface and implemented at the Electrical Power Research Institute's Instrumentation and Control Center located at Tennessee Valley Authority's Kingston fossil power plant. Results are presented for 51 process signals with an average absolute estimation error of {approx}1.7% of the mean value, and sample drift detection performances are shown.