Residual evaluation for fault detection and isolation with RCE neural networks

In this paper a neural network based residual evaluation concept for fault diagnosis is introduced. Based on the idea of emphasizing the evaluation part of a diagnostic concept in contrast to most existing schemes a restricted Coulomb energy neural network (RCE) is employed to classify residuals coming from a standard parameter estimation procedure. The classification aims at the detection and isolation of different faults in the process under supervision. The developed scheme is applied to an industrial actuator benchmark problem which was especially designed for the purpose of comparison of different fault diagnosis techniques. The presented results prove the capability of the presented scheme