Neural network-based failure rate prediction for De Havilland Dash-8 tires

An artificial neural network (ANN) model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered 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 r...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence 2006-09, Vol.19 (6), p.681-691
Main Authors: Al-Garni, Ahmed Z., Jamal, Ahmad, Ahmad, Abid M., Al-Garni, Abdullah M., Tozan, Mueyyet
Format: Article
Language:English
Subjects:
Aircraft reliability
Back-propagation
Failure rate
Neural networks
Preventive maintenance
Weibull regression model
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Summary:An artificial neural network (ANN) model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered 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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2006.01.005