Abstract
A new technique for online process fault diagnosis using fuzzy neural networks is described. The fuzzy neural network is obtained by adding a fuzzification layer to a conventional feedforward neural network. The fuzzification layer converts the increment in each online measurement and controller output into three fuzzy sets: increase, steady, and decrease, with corresponding membership functions. The feedforward neural network then classifies abnormalities represented by fuzzy increments in online measurements and controller outputs into various categories. The fuzzification layer can compress training data and thereby ease training effort. Robustness of the diagnosis system is enhanced by adopting a fuzzy approach in representing abnormalities in the process. The proposed technique has been successfully applied to the fault diagnosis of a continuous stirred tank reactor.
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