A bearing fault diagnosis approach based on an improved neural network combined with transfer learning
Ruoyu Li,
Yanqiu Pan,
Qi Fan
et al.
Abstract:In modern industrial systems, bearing failures account for 30–40% of industrial machinery faults. Traditional convolutional neural network suffers from gradient vanishing and overfitting, resulting in a poor diagnostic accuracy. To address the issues, a new bearing fault diagnosis approach was proposed based on an improved AlexNet neural network combined with transfer learning. After decomposition and noise-reduction, reconstructed vibration signals were transformed into 2D images, then input into the improved… Show more
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