Artificial neural network application of modeling failure rate for Boeing 737 tires

This paper presents an application of artificial neural network (ANN) technique for conducting the reliability analysis of Boeing 737 tires. For this purpose, an ANN model utilizing the feed-forward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are the flight operational time and the number of landings as independent variables and the output is the failure rate of the tires. Two years of data are used for failure rate prediction model and validation. Model validation, which reflects the suitability of the model for future predictions, is performed by comparing the predictions of the model with that of Weibull regression model. The results show that the failure rate predicted by the ANN is closer in agreement with the actual data than the failure rate predicted by the Weibull model. The present work also identifies some of the common tire failures and presents representative results based on the established model for the most frequently occurring tire failure.