# Multi-Scale Rolling Bearing Fault Diagnosis Method Based on Transfer Learning
## 3 Methods
### Overview of the Proposed Model's Architecture
The dataset was collected at a sampling rate of 50 KHz and includes operating conditions at speeds of 600 rpm, 800 rpm, and 1000 rpm. The data are categorized into inner race fault (IF), outer race fault (OF), rolling element fault (BF), and normal (N) conditions, supporting a variety of fault diagnosis and machine learning research scenarios. For this study, data at speeds of 600 rpm and 1000 rpm were selected for model validation, with the bearing health conditions shown in Figure 4.4

To validate the effectiveness of the method proposed in this paper, this study utilizes two types of datasets, CWRU and JNU, to create various tasks. Initially, vibration signals are segmented using data overlap sampling techniques, with each sample containing 1024 data points. This approach aims to increase the sample size and enhance the robustness of the model. Further, to assess the model's capability to classify different unknown fault types, as well as its adaptability to variations in category numbers between source and target tasks, we select multiple sample sets under different working conditions for the grouping \begin{table}
\begin{tabular}{c c c} \hline \hline
**Label** & **Bearing Status** & **Fault Diameter (mil)** \\ \hline
0 & N & - \\
1 & BF & 7 \\
2 & BF & 14 \\
3 & BF & 21 \\
4 & IF & 7 \\
5 & IF & 14 \\
6 & IF & 21 \\
7 & OF & 7 \\
8 & OF & 14 \\
9 & OF & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: CWRU dataset Information. \begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Bearing Status** \\ \hline
0 & N \\
1 & IF \\
2 & OF \\
3 & BF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: JNU dataset information. of source and target tasks. The specific details of each task in the CWRU and JNU datasets are displayed in Table 3.

### Experimental Results and Comparative Analysis

Our approach is evaluated on two standard datasets, CWRU and JNU, and compared to current advanced diagnostic methods. The comparative results are shown in Tables 4 and 5. As indicated in Tables 4 and 5, the proposed model achieves the highest _all_acc_ and _known_all_ in most diagnostic tasks. Notably, our model achieves average accuracy of 90% on both datasets, reaching an optimal level. This not only reflects the model's excellent classification ability across various categories but also demonstrates its outstanding performance in overall tasks. To more intuitively observe the differences in



In the analysis of the fault diagnosis results for the JNU dataset, it can be observed that our model demonstrates relatively low fluctuations in both _all_acc_ and _known_all_, indicating its high diagnostic stability. Even in instances where the accuracy is slightly below the optimal results, the model still exhibits robustness in handling diverse tasks and complex environments. For the tasks on the CWRU dataset, with the exception of C31 and C32, all results also indicate optimal performance. This further indicates that our model can reach high accuracy in the majority of tasks. These encouraging experimental results demonstrate that our fault diagnosis method has advantages in accurately identifying unknown categories. By validating the model's performance across different tasks, the study confirms its generalizability and adaptability, providing robust support for future research and practical applications in the field of fault diagnosis. The analysis further illustrates that the MBDCNet model proposed in this article adopts key strategies in its design, showcasing exceptional capability in bearing fault diagnosis. The model's use of dynamic convolution, batch normalization, activation functions, average pooling, and max pooling has been proven to enable the better capture of fault characteristics in bearing vibration signals. These components effectively enhance the model's receptive field and spatiotemporal adaptability. The experimental results effectively validate the crucial role of the pre-training phase.