On human to artificial neural network in maintenance applications - Ozor, P.; O. Onyegegbu, S.; C. Agunwamba, J.; Mbohwa, C. (International Journal of Engineering and Technology, 2019)
Maintenance is key to meeting sustainability and productivity expectations of repairable industrial systems. The upsurge of growth in complexity of processing equipment has since raised the maintenance function beyond the comprehension of human neural networks, thereby necessitating recourse to artificial intelligence techniques. The nexus between human neural network and artificial neural network was x-rayed in this paper. Attempt was made to present the foundations of artificial neural network modeling techniques and the major contributors to the development of the field. Some of the terminologies associated with human and artificial neural networks were defined and explained with relevant figures. The capability of artificial neural network in predicting the reliability of a case study industrial system, with data taken from a beverage company was illustrated. The two-layer network has one input and output layer apiece, as well as a hidden layer of twenty neurons. The architecture is a feed forward back propagation neural network, while the Levemberg-Marquart training algorithm was employed. It was observed that reliability predictions obtained from ANN model proved very good, considering the very low values of MSE (1.0256X10-3) and high value of the regression (0.998742).Â Â