On-line process fault diagnosis using fuzzy neural networks

The paper describes a new technique for online process fault diagnosis using fuzzy neural networks. The fuzzy neural network considered in this paper is obtained by adding a fuzzification layer to a conventional feed-forward 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 feed-forward 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. Applications of the proposed technique to the fault diagnosis of a continuous stirred tank reactor system demonstrate that the technique is robust to measurement noise, capable of diagnosing incipient faults, and requires fewer training data examples than a conventional network approach