Robustness test of an incipient fault detector artificial neural network

Addresses the issue of robustness in artificial neural networks subject to small input perturbations. The robustness in artificial neural networks is studied using the concept of input-output sensitivity analysis applied to an incipient fault detector artificial neural network (IFDANN). The IFDANN was designed to detect winding insulation faults and bearing wear in single-phase squirrel-cage induction motors. Modification of the IFDANN, with the intention of increasing its robustness to input noise during real-time applications, is discussed. Analytical and simulation results are presented to show the significant improvement in robustness of the modified IFDANN for operation with noisy measurements