(2006) Neural network-based failure rate prediction for De Havilland Dash-8 tires. Engineering applications of Artificial Intelligence, 19. pp. 681-691.
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Abstract
An artificial neural network (ANN) model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the twolayered feed-forward back-propagation 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 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.
| Item Type: | Article |
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| 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/335 |