A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network
发布时间：2025-04-30
点击次数：
- 发布时间：
- 2025-04-30
- 论文名称：
- A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network
- 发表刊物：
- MEASUREMENT SCIENCE AND TECHNOLOGY
- 摘要：
- Traditional methods used for intelligent condition monitoring and diagnosis significantly depend on manual feature extraction and selection. To address this issue, a transfer learning-convolutional neural network (TLCNN) based on AlexNet is proposed for bearing fault diagnosis. Firstly, a 2D image representation method converts vibration signals to 2D timefrequency images. Secondly, the proposed TLCNN model extracts the features of the 2D time-frequency images and achieves the classification conditions of the bearing, which is faster to train and more accurate. Thirdly, t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize the feature learning process to demonstrate the feature learning ability of the proposed model. The experimental results verify that the proposed fault diagnosis model has higher accuracy and has much better robustness against noise than other deep learning and traditional methods.
- 合写作者：
- Ma, P; Zhang, HL; Fan, WH; Wang, C; Wen, GR; Zhang, Xining
- 卷号：
- 30(5)
- 是否译文：
- 否
- 发表时间：
- 2019-05-01