Time-Series Prediction for Amount of Airworthiness Based on Time-Delay Neural Networks

Ali Tatli,Hikmet Karakoc,Sinem Kahvecioglu

doi:10.5755/j01.eie.26.5.25843

Ali Tatli, Hikmet Karakoc + Show 1 more

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https://doi.org/10.5755/j01.eie.26.5.25843

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Abstract

Troposphere and the first stratum of the stratosphere are intensely utilized atmosphere layers for the aviation activities. Due to the different performances, capabilities, designs, and equipment of the aerial vehicles, meteorological weather events that occur in the troposphere affect these vehicles at different levels during their aeronautical activities. Although simple aircrafts are more sensitive to the effects of meteorological events, they are especially preferred by flight training organizations (FTOs) in pilotage training when they are considered in terms of maintenance and equipment costs. In cases where inexperienced pilot candidates and simple aircrafts that are more vulnerable to weather events come together, analysis and prediction of meteorological parameters becomes more important in terms of preventing accidents and reducing risks, as well as proper planning for flight and maintenance. The purposes of this study are, first, to derive flight availability time-series for two different types of aircraft according to visual flight rules by using Meteorological Terminal Air Report (METAR), and then to establish and evaluate a prediction model by using Time-Delay Neural Networks (TDNNs).

Highlights

The flight activities are directly affected by Meteorological parameters and events
The purpose of this study is to introduce a model to predict the airworthiness to support preventing accidents and reducing risks in flight training activities and to proper scheduling for flight and maintenance
In respect of the training and testing phases, the underlined values presented in the Table II is the network which has the least Root Mean Square Error (RMSE) among the trained 9280 delayed network models, the training with the least RMSE was obtained in the model with the activation function of Elliot sigmoid at 38 hidden layer neurons, using the past two time-series data {t - 2, t - 1} and the month information as inputs

Summary

Introduction

The flight activities are directly affected by Meteorological parameters and events. Much so that meteorological information plays an important role in planning, such as flight programs, travel times, routes, and flight levels. Non-optimal weather conditions may cause delay, cancel or re-route of the flights for safety reasons. Predicting unfavourable weather conditions and flight times to reduce meteorological risks to ensure flight safety is a substantial factor in the planning of maintenance activities, which is one of the flight safety requirements.

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