A neural network approach to detect winding faults in electrical machine

In this paper, Neural Network (NN) approach is developed and utilised to detect winding faults in an electrical machine using the samples data of electrical machine in both the healthy and different fault conditions (i.e. shorted-turn fault, phase-to-ground fault and coil-to-coil fault). This is done by interfacing a data acquisition device connected to the machine with a computer in the laboratory. Thereafter, a two-layer feed-forward network with Levenberg-Marquardt back-propagation algorithm is created with the collected input dataset. The NN model developed was tested with both the healthy and the four different fault conditions of the electrical machine. The results from the NN approach was also compared with other results obtained by determining the fault index (FI) of an electrical machine using signal processing approach. The results show that the NN approach can identify each of the electrical machine condition with high accuracy. The percentage accuracy for healthy (normal), shorted-turn, phase-to-ground and coil-to-coil fault conditions are 99, 99.6, 100 and 100% respectively.