Diagnosis of Bearing Faults Under Variable Speed Conditions Using Deep Learning

Öz

Bearings are fundamental and delicate elements directly influencing performance, efficiency, stability, and operational lifespan. However, harsh and fluctuating operating conditions not only jeopardize the safe working environment but also lead to abrupt and unforeseen component faults, resulting in economic losses. Diagnosing faults in bearings operating under variable speed conditions necessitates a shift from traditional methods towards more intricate signal processing techniques and artificial intelligence models with more challenging interpretations. Nevertheless, this research article aims to significantly reduce computational burden and complexity by employing simpler and more straightforward models both in the process of feature extraction and classification, utilizing deep learning methodologies. The research article encompasses the transformation of raw vibration data obtained from bearings operating under variable speed conditions into visual representations and their subsequent classification using the Long Short-Term Memory (LSTM), one of the deep learning models. The developed LSTM-based fault classification model, trained with very limited data, achieves 100% accuracy in classifying four different states of the bearing.

Anahtar Kelimeler

• Bearing
• fault diagnosis
• deep learning
• LSTM
• signal to image mapping

Değişken Hız Koşullarında Rulman Arızalarının Derin Öğrenme Kullanılarak Teşhisi

Öz

Rulmanlar, performansı, verimliliği, stabiliteyi ve operasyonel ömrü doğrudan etkileyen temel ve hassas bileşenlerdir. Ancak, zorlu ve değişken çalışma koşulları, yalnızca güvenli çalışma ortamını tehlikeye atmakla kalmaz, aynı zamanda ani ve öngörülemeyen bileşen arızalarına yol açarak ekonomik kayıplara neden olmaktadır. Değişken hız koşulları altında çalışan rulmanlarda arıza teşhisi, geleneksel yöntemlerden daha karmaşık sinyal işleme tekniklerini ve yorumlanması daha zor yapay zekâ modellerini gerektirir. Buna rağmen, bu araştırma makalesi, hem özellik çıkarma hem de sınıflandırma sürecinde daha basit ve anlaşılır modeller kullanarak hesaplama yükünü ve model karmaşıklığını önemli ölçüde azaltmayı amaçlamaktadır. Araştırma makalesi, değişken hız koşulları altında çalışan rulmanlardan elde edilen ham titreşim verilerinin görsel temsillere dönüştürülmesini ve ardından derin öğrenme modellerinden biri olan LSTM ile sınıflandırılmasını kapsamaktadır. Geliştirilen LSTM tabanlı arıza sınıflandırma modeli, oldukça sınırlı verilerle eğitildiğinde, rulmanın dört farklı durumunu %100 doğrulukla sınıflandırmayı başarmaktadır.

Anahtar Kelimeler

• Rulman
• Arıza Teşhisi
• Derin Öğrenme
• LSTM
• Sinyal Görüntü Haritalama

Kaynakça

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