Uçak Motoru Sağlığı için Uzun-Kısa Süreli Bellek Yöntemi ile Öngörücü Bakım

Year 2019, Volume: 12 Issue: 2, 103 - 109, 30.04.2019

Merve Ayyüce Kızrak , Bülent Bolat

https://doi.org/10.17671/gazibtd.495730

Cited By: 5

Abstract

Otomotiv, uçak ve fabrika otomasyonu gibi özellikle maliyetli motor bakımı gerektiren uygulamalarda öngörücü bakım önemli bir yer almaktadır. Hem iş güvenliği hem de araçlardan sağlanacak verim bakımından motorların bakım periyotlarını önceden kestirmek ve buna göre iş yönetim stratejisi geliştirmek önemlidir. Öngörücü bakım için motorlardan alınan sensör verileri motorun yıpranma süresini ve seviyesini belirlemekte kullanılmaktadır. Çalışmada Uzun-Kısa Süreli Bellek (LSTM) yapısı kullanılarak, uçak motorlarının kalan yaşam ömürlerinin tahmin edilmesi amaçlanmıştır. NASA tarafından sunulmuş olan bir veri kümesi üzerinde LSTM yapısı test edilmiştir ve elde edilen sonuçlar farklı yöntemlerle kıyaslanmıştır. Yapılan uygulamaların sonucunda en yüksek sınıflandırma başarımı %98,876; en düşük ortalama mutlak hata ise 1,343 olarak LSTM ile elde edilmiştir.

Keywords

öngörücü bakım, derin öğrenme, uzun-kısa vadeli bellek, makine öğrenmesi, özyinelemeli sinir ağları, uçak motor sağlığı, yapay sinir ağları

Predictive Maintenance of Aircraft Motor Health with Long-Short Term Memory Method

Abstract

Predictive maintenance is an important part of applications that require costly engine maintenance, such as automotive, aircraft and factory automation. It is important to anticipate the maintenance periods of the engines and develop a business management strategy accordingly in terms of both work safety and efficiency. For predictive maintenance, the sensor data from the motors were used to determine the wear time and level of the engine. In this study, a solution based on deep learning is proposed as an alternative to traditional regression and classification methods. The NASA Turbofan Engine Corruption Simulation data set was studied by using Long-Short Term Memory (LSTM), one of the deep learning models and known to make successful predictions on time-dependent data such as time series. During the simulations, the highest classification performance and the lowest mean absolute error were obtained by LSTM as 98,876% 1.343 respectively.

Keywords

predictive maintenance, deep learning, long-short term memory, artificial neural networks, machine learning, aircraft health system

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