Fault Diagnosis of Dynamic Systems Using Neural Networks

Abstract A method for detecting faults of nonlinear systems is developed using dynamic nonlinear predictor models which are realized with neural networks. Separate predictor models are identified for the process operating normally and for the situations where one of the potential faults has occurred. In monitoring the state of the process, the best fitting predictor of the bank is selected according to the Bayes rule. Radial basis function networks are used in identifying dynamic predictor models and the parameters of the networks are estimated with the orthogonal least squares algorithm. The performance of the proposed method is demonstrated in the simulation studies of a jacketed reactor.