(2006) FailureRate Prediction for De Havilland Dash8 Tires Employing NeuralNetwork Technique. AIAA Journal of Aircraft, 43 (2). pp. 537543.

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Abstract
An artificial neuralnetwork model for predicting the failure rate of De Havilland Dash8 airplane tires utilizing the twolayered feedforward backpropagation algorithm as a learning rule is developed. The inputs to the neural network are independent variables, and the output is the failure rate of the tires. Six years of data are used for model building and validation. Model validation, which reflects the suitability of the model for future prediction, is performed by comparing the predictions of the model with that of theWeibull regression model. The results show that the failure rate predicted by the artificial neural network more closely agrees with the actual data than the failure rate predicted by the Weibull model.
Item Type:  Article 

Subjects:  Aerospace 
Department:  College of Engineering and Physics > Aerospace Engineering 
Depositing User:  AHMAD JAMAL 
Date Deposited:  15 Mar 2008 14:02 
Last Modified:  01 Nov 2019 13:23 
URI:  http://eprints.kfupm.edu.sa/id/eprint/287 