Öznitelik Odaklı Sensor Verisi Bazlı Uçak Motorları Geriye Kalan Faydalı Ömür Tahminleme

Uçak motorunun durumu, uçağın güvenliğini, uçuş kalitesini ve çalışmasını doğrudan etkiler. Uçak motorları için belirti izleme faaliyetleri, motorun kalan faydalı ömrünü tahmin etmek için bir önceden önlem alınmasını sağlayabilecek bir avantaj yaratabilir. Uçak motoru yapıları hem soyut hem de somut bileşenlerle karmaşık olduğundan, motor faaliyet bozulma sürecini göstermek oldukça zahmetlidir. Bu yazıda, kalan faydalı ömür tahmini doğruluğunu iyileştirmek için öznitelik odaklı çerçeve geliştirilmiştir. Bu çerçeve, motorlardan gelen gereksiz duyusal girdileri ortadan kaldırır ve hesaplama maliyetlerini düşürür. Bir uygulama örneği olarak, sensör verilerine dayalı olarak uçak motorunun kalan faydalı ömrünü tahmin etmek için geliştirilmiş öznitelik odaklı çerçeve kullanılmıştır. Sonuçlar, diğer yöntemleri uygulamadan önce, birçok girdi özelliğine sahip sistemlerin, maliyeti düşürmek için özellik uyarlama prosedürlerine ihtiyaç duyduğunu, ancak kalan faydalı ömrü tahmin etmek için kesinliği artırdığını göstermektedir.

Anahtar Kelimeler:

Uçak Motor, Veri Madenciliği, Makine Öğrenmesi, Destek Vektör Makineleri, Lojistik Regresyon, Doğrusal Regresyon, NASA Turbofan Veri Seti, Öznitelik Seçme, PCA

Feature-Oriented Remaining Useful Life Prediction of Aircraft Engines Based on Sensor Data

Aircraft engine’s condition straightforwardly influences the security, unwavering quality, and operation of the aircraft. Prognostics and wellbeing administration for aircraft engines can give a advantage to estimate the remaining useful life of the engine and can enable to take precautionary actions in advanced. Be that as it may, aircraft engine frameworks are complex with both intangible and dubious components, it is troublesome to demonstrate the complex degradation process. In this article, the remaining useful life estimation is developed to improve feature -oriented framework. This frame eliminates unnecessary sensory inputs from engines and reduces calculation costs. As an application example, the developed feature -oriented frame has been used to estimate the remaining use of the aircraft engine based on sensor data. The results show that before applying other methods, systems with many input characteristics need feature adaptation procedures to reduce costs, but increase the certainty to estimate the remaining useful life.

Keywords:

Aircraft engine, data mining, machine learning, support vector machines, logistics regression, linear regression, nasa turbofan data set, attribute selection, PCA,

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