# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 3 Experimental Results
### Comparison with Existing Methods  
To evaluate the effectiveness of model compression, Table 4 presents the parameter counts and inference times of both the teacher and student models. The results demonstrate that knowledge distillation significantly reduces model complexity, decreasing the parameter count of the student model from 32.14 k to 8.74 k, a reduction of 72.8%. Concurrently, the inference time is shortened from 20.41 ms to 9.72 ms, a decrease of 52.4%. This indicates that knowledge distillation effectively balances model efficiency with computational performance.  
To further assess the comprehensive performance of the proposed method, a comparative analysis is conducted against ResNet [20] and various lightweight networks, including  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Model Parameters** & **Inference Time** \\ \hline Teacher model & 32.14k & 20.41 \\ Student model & 8.74K & 9.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compression result of the teacher and student model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Diagnostic Strategies**} & \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Average Accuracy**} \\ \cline{3-5}  & & & **Our Dataset** & **PU Dataset** \\ \hline Method 1 & MobileNet & Time domain & 85.7\% & 86.3\% \\ Method 2 & MobileNet & Time-frequency & 88.5\% & 89.7\% \\ Method 3 & MobileNet+Distillation & Time domain & 88.3\% & 88.6\% \\ Method 4 & MobileNet+Distillation & Time-frequency & 90.7\% & 91.3\% \\ Method 5 & MobileNet+Distillation & Time domain & 90.1\% & 90.6\% \\
**+Domain Adaptation** & & & & \\
**Method 6** & **MobileNet+Distillation** & **Time-frequency** & **93.5\%** & **94.2\%** \\
**+Domain Adaptation** & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Diagnostic accuracy under different diagnostic strategies.  
MobileNet [21], ShuffleNet [22], GhostNet [23], and CGhostNet [24]. The evaluation metrics encompass diagnostic accuracy, model parameter count, model size, inference time, and computational complexity (FLOPs), as detailed in Table 5.  
The experimental results reveal that ResNet achieves the highest accuracy (98.21%), but it comes with a substantial parameter count of 743.62 k and an inference time of 203.56 ms, resulting in high computational and storage costs, making it unsuitable for real-time or embedded applications. In contrast, the proposed method contains only 8.74k parameters, representing reductions of 98.82%, 72.8%, and 65.8% compared to ResNet, MobileNet, and ShuffleNet, respectively. Although the parameter count is slightly higher than that of CGhostNet (an increase of 4.5%), the accuracy improves to 97.69%, significantly outperforming other lightweight models and approaching the performance of ResNet. Moreover, the inference time is merely 9.91 ms, demonstrating superior performance among lightweight architectures and ensuring the feasibility of real-time fault diagnosis.  
Additionally, model size is a critical factor for deployment on edge computing devices. GhostNet and CGhostNet exhibit the smallest model sizes (67.5 KB and 46.0 KB, respectively), while the proposed method requires only 51.2 KB--slightly higher than CGhostNet but lower than GhostNet--while achieving significantly higher accuracy (97.69% vs. 85.43% and 87.92%). Therefore, the proposed method demonstrates strong applicability in embedded systems with limited computational resources. To sum up, the proposed method achieves an optimal balance between model size, computational complexity, inference time, and diagnostic accuracy. It maintains high accuracy while significantly reducing computational overhead, demonstrating its effectiveness for real-time fault diagnosis in embedded systems.