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

Al-Garni, Ahmed Z. and Jamal, Ahmad (2008) ARTIFICIAL NEURAL NETWORK APPLICATION OF MODELLING FAILURE RATE FOR BOEING 737 TIRES. In: Applied Simulation and Modeling Conference 2008, 23-25 June, 2008., Corfu, Greece. (Submitted)

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Abstract

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.



Item Type:Conference or Workshop Item (Paper)
Date:23 June 2008
Subjects:Aerospace
Divisions:College Of Engineering Sciences > Aerospace Engineering Dept
Creators:Al-Garni, Ahmed Z. and Jamal, Ahmad
Email:algarni@kfupm.edu.sa, ahmadj@kfupm.edu.sa
ID Code:337
Deposited By:AHMAD JAMAL
Deposited On:15 Mar 2008 17:01
Last Modified:12 Apr 2011 13:05

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