Neural network-based failure rate for Boeing-737 tires

This paper presents an artificial neural network (ANN) model for forecasting the failure rate of Boeing-737 airplane tires. A neural model is developed using the backpropagation algorithm as a learning rule. The inputs to the neural network are independent variables and the output is the failure rate of the tire. A comparison of the neural model with the Weibull model is made for validation purposes. It is found that the failure rate predicted by the ANN is closer in agreement with the real data than the failure rate predicted by the Weibull model.