Aircraft turbines time-to-failures process modeling using RBF NN

Purpose The purpose of this paper is to formulate a specialized artificial neural network algorithm utilizing radial basis function (RBF) for modeling of time to failure of aircraft engine turbines. Design/methodology/approach The model uses training failure data collected from operators of turbopro...

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Bibliographic Details
Published in:Journal of quality in maintenance engineering 2020-05, Vol.26 (2), p.249-259
Main Authors: Al-Garni, Ahmed Z, Abdelrahman, Wael G, Abdallah, Ayman M
Format: Article
Language:English
Subjects:
Aircraft
Aircraft design
Aircraft engines
Algorithms
Artificial neural networks
Back propagation
Back propagation networks
Computing time
Critical components
Engines
Failure
Failure analysis
Failure rates
Fault diagnosis
Model accuracy
Neural networks
Neurons
Optimization techniques
Preventive maintenance
Propagation
Radial basis function
Turbines
Turboprop aircraft
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Summary:Purpose The purpose of this paper is to formulate a specialized artificial neural network algorithm utilizing radial basis function (RBF) for modeling of time to failure of aircraft engine turbines. Design/methodology/approach The model uses training failure data collected from operators of turboprop aircraft working in harsh desert conditions where sand erosion is a detrimental factor in reducing turbine life. Accordingly, the model is more suited to accurate prediction of life of critical components of such engines. The used RBF employs a closest neighbor type of classifier and the hidden unit’s activation is based on the displacement between the early prototype and the input vector. Findings The results of the algorithm are compared to earlier work utilizing Weibull regression modeling, as well as Feed Forward Back Propagation NN. The results show that the failure rates estimated by RBF more closely match actual failure data than the estimations by both other models. The trained model showed reasonable accuracy in predicting future failure events. Moreover, the technique is shown to have comparatively higher efficiency even with reduced number of neurons in each layer of ANN. This significantly decreases computation time with minimum effect on the accuracy of results. Originality/value Using RBF technique significantly decreases the computational time with minimum effect on the accuracy of results.
ISSN:1355-2511
1758-7832
DOI:10.1108/JQME-05-2018-0036