Diagnosis of Bearing Faults Under Variable Speed Conditions Using Deep Learning

Authors : Gonca Öcalan, İbrahim Türkoğlu

Pages : 1-10

View : 20 | Download : 2

Publication Date : 2025-06-23

Article Type : Research Paper

Abstract :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.
Keywords : Rulman, Arıza Teşhisi, Derin Öğrenme, LSTM, Sinyal Görüntü Haritalama

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