TITLE:
Small Sample Gear Fault Diagnosis Method Based on Transfer Learning
AUTHORS:
Han Zhang, Shihao Liu, Xiyang Wang, Junlong Zhang
KEYWORDS:
Gear Fault Diagnosis, Transfer Learning, CWT, Deep Residual Network, Deep Learning
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.13 No.12,
December
29,
2023
ABSTRACT: Aiming at the problems
of lack of fault diagnosis samples and low model generalization ability of
cross-working gear based on deep transfer learning, a fault diagnosis method
based on improved deep residual network and transfer learning was proposed.
Firstly, one-dimensional signal is transformed into two-dimensional
time-frequency image by continuous wavelet transform. Then, a deep learning
model based on ResNet50 is constructed. Attention mechanism is introduced into
the model to make the model pay more attention to the useful features for the
current task. The network parameters trained by ResNet50 network on ImageNet
dataset were used to initialize the model and applied to the fault diagnosis
field. Finally, to solve the problem of gear fault diagnosis under different
working conditions, a small sample training set is proposed for fault
diagnosis. The method is applied to gearbox fault diagnosis, and the results
show that: The proposed deep model achieves 99.7% accuracy of gear fault
diagnosis, which is better than the four models such as VGG19 and MobileNetV2.
In the cross-working condition fault diagnosis, only 20% target dataset is used
as the training set, and the proposed method achieves 93.5% accuracy.