ARTIFICIAL NEURAL NETWORK APPLICATION OF MODELLING FAILURE RATE FOR BOEING 737 TIRES

This paper presents an application of artificial neural network technique for predicting the failure rate of Boeing 737 tires. For this purpose, an artificial neural network model utilizing the feed-forward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are the independent variables and the output is the failure rate of the tires. Two years of data is 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 artificial neural network 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

FAILURE RATE ANALYSIS OF BOEING 737 BRAKES EMPLOYING NEURAL NETWORK

The failure rate analysis of brake assemblies of a commercial airplane, i.e., Boeing 737, is analyzed using the artificial neural network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. Three years of data are used for model building and validation. The results show that the failure rate predicted by neural network is closer in agreement with the actual data than the failure rate predicted by the Weibull model. Results also indicate that neural network can be effectively integrated into an aviation maintenance facility computerized material requirement planning system to forecast the number of brake assemblies needed for a given planning horizon