Comparison of neural network models for process fault detection and diagnosis problems

Two neural network models are compared using a process fault detection and diagnosis problem. Process fault detection and diagnosis using steady-state information is a nonlinear pattern recognition problem. In such problems the measurement pattern vectors are noisy and collection of a complete training data set is time consuming and in some cases impossible. The training data were obtained from a simulated chemical process. The process consists of a reactor and a distillation column. The dynamically capacity allocating network models were found to have the best performance as compared to backpropagation-type network models. These networks can also be used for continuously learning systems and therefore the difficulties of training data collection are avoided.<>