What is it about?
Our study explores the vibration characteristics of gear samples with symmetric (αd = 20°) and asymmetric (αd = 30°) tooth profiles. We investigated the effects of different levels of tooth root cracks (25%−50%−75%−100%) on vibration amplitudes. The primary aim was to determine if tooth profile modifications could enhance the diagnosis of different tooth crack levels using a CNN-based method. Additionally, we examined the influence of DSPA configuration on vibration amplitudes of a one-stage spur gearbox.
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Why is it important?
What sets this research apart is its novelty; no similar study has experimentally addressed the vibration responses of spur gears with asymmetric teeth and investigated whether the tooth profile modifications could improve the classification accuracy of deep learning algorithms in the literature.
Perspectives
This research contributes to the gearbox health management field and highlights the potential of deep learning algorithms in improving diagnosis accuracy and enhancing machine performance.
Prof.Dr. Fatih Karpat
Uludag Universitesi
Read the Original
This page is a summary of: A comparative experimental research on the diagnosis of tooth root cracks in asymmetric spur gear pairs with a one-dimensional convolutional neural network, Mechanism and Machine Theory, October 2024, Elsevier,
DOI: 10.1016/j.mechmachtheory.2024.105755.
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