Identification of Health Status Stages of Wind Turbine High Speed Shaft Bearing with Deep Learning

Year 2024, EARLY VIEW, 1 - 1

Gonca Öcalan , İbrahim Türkoğlu

https://doi.org/10.2339/politeknik.1388385

Abstract

Mechanical components in wind turbines with an unstable operating environment under variable weather conditions are at a very high risk of wear. This situation brings about sudden unexpected stops of components and high maintenance costs. It is of great importance to plan appropriate maintenance times in order to ensure continuity in energy production, prevent unexpected unplanned downtime and minimize maintenance costs. Therefore, before a component failure occurs, the health status must be carefully monitored and maintenance periods must be planned according to the wear and tear process. In this paper, in order to evaluate the health status of a real wind turbine high-speed shaft bearing, the healthy, degradation and fault stages of the bearing are identified by a deep learning based classification model. In the proposed study, vibration data obtained from a real wind turbine high-speed shaft have been used. The study basically consists of the steps of extracting the features of the vibration data, selecting the features that will effectively reveal the health process of the bearing, obtaining the health index by integrating the selected features, and classifying the health index into stages with the LSTM deep learning model. In the study where four different health stages are defined, an accuracy of 99% has been obtained on the test data.

Keywords

wind turbine, vibration, deep learning, classification of health status

Rüzgâr Türbini Yüksek Hızlı Şaft Rulmanının Sağlık Durumu Aşamalarının Derin Öğrenme İle Belirlenmesi

Abstract

Değişken hava koşulları altında kararsız çalışma ortamına sahip rüzgâr türbinlerinde, mekanik bileşenler oldukça yüksek yıpranma riski altındadır. Bu durum, bileşenlerde ani beklenmedik duruşları ve yüksek bakım maliyetlerini beraberinde getirmektedir. Enerji üretiminde sürekliliği sağlamak, beklenmeyen plansız duruşların önüne geçmek ve bakım maliyetlerini en aza indirgemek amacıyla uygun bakım zamanlarının planlanması oldukça büyük öneme sahiptir. Bundan dolayı bileşende arıza meydana gelmeden önce sağlık durumunun dikkatli bir şekilde takip edilmesi ve bakım periyotlarının yıpranma sürecine göre planlanması gerekir. Bu makalede, gerçek bir rüzgâr türbini yüksek hızlı şaft rulmanının sağlık durumunun değerlendirilmesi amacıyla, rulmanın sağlıklı, bozulma ve arızalanma aşamaları derin öğrenme tabanlı sınıflandırma modeli ile belirlenmektedir. Önerilen çalışmada gerçek bir rüzgâr türbini yüksek hızlı şaftından elde edilen titreşim verileri kullanılmaktadır. Bu çalışma, temel olarak titreşim verilerine ait özelliklerinin çıkarılması, rulmanın sağlık sürecini etkin bir şekilde ortaya çıkaracak özelliklerin seçilmesi, seçilen özelliklerin bütünleştirilerek sağlık indeksinin elde edilmesi ve sağlık indeksinin aşamalara bölünerek Uzun Kısa Süreli Bellek (Long Short Term Memory – LSTM) derin öğrenme modeli ile sınıflandırılması adımlarından oluşmaktadır. Dört farklı sağlık aşamasının belirlendiği çalışmada test verileri üzerinde %99 oranında doğruluk değeri elde edilmiştir.

Keywords

rüzgâr türbini, titreşim, derin öğrenme, sağlık durumunun sınıflandırılması

Ethical Statement

Bu makalenin yazar(lar)ı çalışmalarında kullandıkları materyal ve yöntemlerin etik kurul izni ve/veya yasal-özel bir izin gerektirmediğini beyan ederler.

Supporting Institution

TUBİTAK – BİDEB 2211/C Yurtiçi Öncelikli Alanlar Doktora Burs Programı

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