A new deep transfer learning method for bearing fault diagnosis was proposed. By directly learning the nonlinear mapping relationship between the original vibration signals and the health state of the bearing, this method automatically extracted the fault features and identified bearing faults from end to end.
The purpose of bearing fault diagnosis under varying conditions is to obtain a model which is trained based on multi-source domains with labels and a target ...
Feb 13, 2023 · Abstract: The traditional deep learning-based bearing fault diagnosis approaches assume that the training and test data follow the same ...
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Apr 22, 2024 · This work proposes an innovative cross-domain fault diagnosis framework based on deep transfer convolutional neural network and supervised joint ...
The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults ...
Oct 9, 2023 · A parameter transfer method is introduced to solve the deep model overfitting problem caused by insufficient label samples in the target domain.
Missing: bearings | Show results with:bearings
Jan 2, 2024 · In this paper, a fault diagnosis method for rolling bearing based on deep adversarial transfer learning with transferability measurement is ...
Oct 9, 2023 · In this paper, a transfer learning-based feature fusion convolutional neural network approach for bearing fault diagnosis is proposed.
Rolling bearings are the vital components of rotary machines. The collected data of rolling bearing have strong noise interference, massive unlabeled ...
Therefore, it is of great practical significance to study the new method of rolling bearings fault diagnosis under the imbalance samples. Transfer learning [4] ...