## 4 Conclusions  
To address the challenges of data imbalance in real-world engineering and the complexity of applying neural networks in real-time monitoring, this study introduces a lightweight fault diagnosis model for mechanical equipment based on MobileNet, enhanced with KDE-ADASYN and an SE block. The KDE-ADASYN algorithm adaptively generates fault samples to balance the data, while FFT converts time-domain signals into frequency-domain signals. The integration of the SE block further enhances the model's fault feature recognition capabilities. In fault simulation experiments, four metrics--accuracy, precision, recall, and F1 score--were employed to thoroughly evaluate the diagnostic performance on various imbalanced datasets. The results demonstrated that the proposed method consistently delivers excellent performance. Additionally, the validation process explored the necessity of each component in the method, with comparative experiments confirming that the proposed approach achieved optimal performance across all metrics, while also reducing network parameters and minimizing computational resource consumption.  
In future work, we aim to address several key challenges: First, we will tackle the issues encountered in real-world scenarios, where data typically contain higher levels of noise, more diverse operating conditions, and complex fault scenarios that were not fully captured in this study. Second, we plan to explore the model's ability to generalize across different experimental setups, devices, and operating conditions. Finally, we will investigate multi-modal approaches, integrating vibration data with other sensor modalities (e.g., acoustic or thermal data) to enhance the model's performance and robustness.  
W.L.: Writing--review and editing, Writing--original draft, Validation, Software, Methodology, Conceptualization. W.W.: Writing--review and editing, Supervision, Data curation. X.Q.: Writing--review and editing. Z.C.: Writing--review and editing, Supervision  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Acc (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{E} & BEGSMOTE & 89.14 & 89.35 & 89.14 & 89.13 \\  & ADASYN & 87.39 & 87.60 & 87.39 & 87.38 \\  & GAN & 90.25 & 90.42 & 90.25 & 90.24 \\  & Proposed method & 92.36 & 92.51 & 92.36 & 92.36 \\ \hline \multirow{4}{*}{F} & BEGSMOTE & 92.33 & 92.53 & 92.33 & 92.32 \\  & ADASYN- & 91.41 & 91.60 & 91.41 & 91.41 \\  & GAN & 93.73 & 93.90 & 9373 & 93.72 \\  & Proposed method & 95.22 & 95.43 & 95.22 & 95.21 \\ \hline \multirow{4}{*}{G} & BEGSMOTE & 95.71 & 95.90 & 95.71 & 95.70 \\  & ADASYN & 95.12 & 95.21 & 95.12 & 95.12 \\  & GAN & 96.59 & 96.79 & 96.59 & 96.58 \\  & Proposed method & 97.86 & 98.05 & 97.86 & 97.84 \\ \hline \multirow{4}{*}{H} & BEGSMOTE & 97.94 & 98.14 & 97.94 & 97.94 \\  & ADASYN & 97.72 & 97.91 & 97.72 & 97.70 \\ \cline{1-1}  & GAN & 98.97 & 99.18 & 98.97 & 98.97 \\ \cline{1-1}  & Proposed method & 99.88 & 99.87 & 99.86 & 99.86 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative results of different generation methods.  
Resources, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China [72271200 and 72231008], the Distinguished Young Scholar Program of Shaanxi Province [2023-Q-JC-10], the Open Fund of Intelligent Control Laboratory [ICL-2023-0304], and the Science and Technology Innovation Team of Shaanxi Province [2024 RS-CXTD-28].  
Not applicable.  
The data presented in this study are available upon request from the corresponding author due to privacy restrictions.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.