## 6 Conclusions  
This paper proposes a multi-path information fusion fault diagnosis network for rolling bearings, incorporating a two-dimensional time series transformation module and a multi-path time series extractor. The two-dimensional transformation module extracts key periodic features by extending the original one-dimensional signal into two-dimensional space, while the multi-path time series extractor captures multi-scale features. Extensive experiments conducted on a public dataset demonstrate that the proposed model outperforms existing methods, achieving an average accuracy of 97.82% in fault diagnosis. Ablation experiments validate the effectiveness of each module, showing significant improvements in performance when all modules are used in combination. However, there are several limitations to the proposed approach. Firstly, while the model achieves good performance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**2 DT** & **MPTSE** & **A** & **B** & **C** & **D** \\ \hline \(\checkmark\) & \(\checkmark\) & 97.58\% & 97.08\% & 98.33\% & 99.42\% \\ \(\times\) & \(\checkmark\) & 94.92\% & 94.92\% & 97.58\% & 98.67\% \\ \(\checkmark\) & \(\times\) & 94.92\% & 91.58\% & 96.25\% & 98.58\% \\ \(\times\) & \(\times\) & 92.08\% & 93.33\% & 94.83\% & 92.25\% \\ \hline \hline \end{tabular} \(\checkmark\) indicates that the corresponding module is included, and \(\times\) indicates that it is not included.  
\end{table}
Table 5: Experimental results of module ablation.  
on the CWRU dataset, its robustness to noise and performance on external datasets from different devices remain unverified. Additionally, the two-dimensional transformation method may introduce some loss of long-term dependencies, and the computational complexity of the model may hinder its real-time application in industrial settings. In future work, we plan to focus on improving the model's robustness to noise and enhancing its adaptability to various industrial environments. We will also explore the use of more complex time series processing methods and investigate ways to reduce the model's computational complexity for real-time applications. Furthermore, we will apply the proposed methodology to real-world scenarios and increase the dataset of misclassified samples to build a more robust diagnostic model.  
Conceptualization, C.Z.; methodology, C.Z.; software, C.Z. and Y.L.; validation, C.Z. and Y.L.; formal analysis, Y.L.; investigation, C.Z.; resources, Q.N.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, C.Z., Y.L. and Q.N.; visualization, Y.L.; supervision, Q.N.; project administration, C.Z.; funding acquisition, Q.N. 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 [51674255].  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from Case School of Engineering and are available at [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)), accessed on 18 December 2024.  
The authors would like to thank the anonymous referees and the editors for their helpful comments and suggestions.  
Author [PERSON] was employed by the company China Pingmei Shenma Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
CWTContinuous wavelet transform  
DWTDiscrete wavelet transform  
FFTFast Fourier transform  
DFTDiscrete Fourier transform  
MPTSEMulti-path time series extractor  
T-inceptionTime series inception