Comparison of machine learning algorithms performance in predictive maintenance of turbofan engines

Year 2024, Volume: 13 Issue: 1, 99 - 106, 15.01.2024

Osman Güler

https://doi.org/10.28948/ngumuh.1266541

Abstract

One of the important steps taken within the scope of Industry 4.0 is predictive maintenance studies. In this way, machine and equipment life is extended and working efficiency is increased. Predictive maintenance studies of aircraft and aircraft engines are important, especially since flight safety is of vital importance in the aviation industry. In this study, 10 different machine learning algorithms were used to predict the remaining useful life of turbofan engines and their results were compared. For the trainings, the CMAPSS dataset, which shows the working conditions of turbofan engines under certain conditions, presented by NASA was used. The results of the study show that machine learning models generally achieve similar performance to each other. As a result of the experiments, it was seen that the LDA algorithm was the most successful algorithm.

Keywords

Machine learning, Predictive maintenance, NASA Turbofan dataset, Aircraft engine, Remaining useful life

Turbofan motorlarının kestirimci bakımında makine öğrenimi algoritmaları performanslarının karşılaştırılması

Abstract

Endüstri 4.0 kapsamında atılan önemli adımlardan birisi kestirimci bakım çalışmalarıdır. Bu sayede makine ve ekipman ömrünü uzatılmakta, çalışma verimliliği artırılmaktadır. Özellikle havacılık sanayii alanında uçuş güvenliği hayati önem taşıdığından dolayı, uçakların ve uçak motorlarının kestirimci bakım çalışmaları önemlidir. Bu çalışmada, turbofan motorlarının kalan faydalı ömrünü tahmin etmek için 10 farklı makine öğrenmesi algoritmaları kullanılmış ve sonuçları karşılaştırılmıştır. Eğitimler için NASA tarafından sunulan turbofan motorların belirli koşullar altında çalışma durumlarını gösteren CMAPSS veriseti kullanılmıştır. Çalışma sonuçları makine öğrenmesi modellerinin genel olarak birbirlerine yakın performans elde ettiğini göstermektedir. Yapılan deneyler sonucunda LDA algoritmasının en başarılı algoritma olduğu görülmüştür.

Keywords

Makine Öğrenmesi, Kestirimci Bakım, NASA Turbofan Veri Seti, Uçak Motoru, Kalan Faydalı Ömür

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