Prediction of Aircraft Failure Times Using Artificial Neural Networks and Genetic Algorithms

Abstract

The aviation industry lived through the global economic crisis by mergers and a review of cost cent factors. Maintaining a fleet is more delicate than ever because low price is the focus of demand, but providing safety is the greatest cost. These facts lead to dynamic maintenance of the aircraft in order to eliminate excessive maintenance costs while ensuring safety. Preemptive maintenance can be run only if the failure times of aircraft are predicted ahead of occurrence. This study aims to predict when the failure will happen by aircraft type and age. An application of 60 aircraft, which lived through 532 failures, is modeled with artificial neural networks and genetic algorithms, which are known to be the preferred forecasting tools. The model proposed gives a good forecast correlation rate between the target and actual failure schedules of aircraft.