王海涛 Likes:

Affiliation of Author(s):

机电工程学院

Journal:

IEEE ACCESS

Key Words:

Ball bearings；deep learning；data models；data processing；fault diagnosis；feature extraction；feature detection；image classification；residual neural networks；transfer learning

Abstract:

To address the problem that uneven sample distribution can affect the accuracy and stability of fault diagnosis outcomes, we propose a deep transfer learning-Res2Net-convolutional block attention mechanism model. Firstly, the deep migration technique is used to transfer weights of the imbalanced source domain samples to the balanced target domain, expanding the data samples. Secondly, in the feature extraction and detection phase, All eight residual blocks are embedded in convolution block attention to ensure that interference signals are suppressed and that the key fault features are retained. Multilayer feature fusion extracts faulty sample features from multiple residual blocks of the network, which are combined into the feature fusion layer by parallel splicing. Finally, this model is experimentally validated using two different bearing datasets, and the combined evaluation indexes of the new model under the severe imbalance condition are 95.80% and 95.85%, respectively, demonstrating the feasibility and excellent performance of the model.

First Author:

wanghaitao

Indexed by:

Journal paper

Correspondence Author:

张希恒

Volume:

12/5517-5533

ISSN No.:

2169-3536

Translation or Not:

no

CN No.:

2169-3536

Date of Publication:

2024-01-08