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 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.