Neural network feature detection and process monitoring

The use of artificial neural networks for efficient predictive nonlinear model development from highly dimensioned and ill conditioned monitored process data is addressed. In particular, the problem of process fault detection is considered. A feature detection network topology is used to reduce the dimensionality of the problem and extract from the process data important attributes that indicate the presence of process malfunctions. The ability of the method to detect process faults is demonstrated by a comprehensive simulation of an industrial polymer reactor