所属单位：

机电工程学院

发表刊物：

IEEE ACCESS

关键字：

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

摘要：

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.

第一作者：

王海涛

论文类型：

期刊论文

通讯作者：

张希恒

卷号：

12/5517-5533

ISSN号：

2169-3536

是否译文：

否

CN号：

2169-3536

发表时间：

2024-01-08