DC motor fault diagnosis by means of artificial neural networks

The paper deals with a model-based fault diagnosis for a DC motor realized using artificial neural networks. The considered process was modelled by using a neural network composed of dynamic neuron models. Decision making about possible faults was performed using statistical analysis of a residual. A neural network was applied to density shaping of a residual, and after that, assuming a significance level, a threshold was calculated. Moreover, to isolate faults a neural classifier was developed. The proposed approach was tested in a DC motor laboratory system at the nominal operating conditions as well as in the case of faults. .