# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 6 Conclusions  
This paper focuses on the problem of the signal collected by sensors including a great deal of noise in complex working environments, making the model unable to effectively  
Figure 11: Each denoising method on GASF+SE ResNeXt18. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**SNR (dB)** & **\(-\)4** & **\(-\)2** & **0** & **2** & **4** & **6** \\ \hline CEEMDAN-WHT & 90.2\% & 93.8\% & 94.5\% & 95.7\% & 97.8\% & 98.5\% \\ CEEMDAN-MPE-WHT & 92.0\% & 95.2\% & 96.0\% & 96.5\% & 98.0\% & 99.2\% \\ CEEMDAN-MPE-AWT & 96.2\% & 97.7\% & 98.3\% & 98.7\% & 98.8\% & 99.2\% \\ ICEEMDAN-MPE-AWT & 97.5\% & 98.2\% & 98.8\% & 99.0\% & 99.2\% & 99.3\% \\ \hline \end{tabular}
\end{table} 
Table 7: Accuracy of each denoising method on GASF+ TSE-ResNeXt18. obtain the fault characteristics from the signal. The ICEEMDAN-MPE-AWT method for noise reduction was proposed. Firstly, the principle of ICEEMDAN was introduced, and the problems existing in the CEEMDAN algorithm were solved. The principle of MPE and AWT denoising was then introduced, followed by the implementation of the ICEEMDAN-MPE-AWT denoising method proposed in this paper. In addition, a transfer learning model based on GASF and SE-ResNeXt was proposed to diagnose gearbox faults, and the experimental comparison with TICNN, GASF+TSE-ResNeXt18 and other models verified that the raised model has higher precision in gearbox fault diagnosis. In addition, experimental analysis and research were conducted on the performance of the noise reduction approach raised in this paper. Firstly, the gearbox fault dataset with and without noise reduction was fed into the model for training. The experimental outcomes showed that the training effect of the dataset after noise reduction is significantly better than that of the dataset without noise reduction. Experimental results also showed that the raised approach has good noise reduction performance and can ensure the accuracy of the model. Compared with CEEMDAN-MPE-AWT, ICEEMDAN-WHT and ICEEMDAN-MPE-WHT, the proposed ICEEMDAN-MPE-AWT in this paper has better noise reduction performance. The method proposed in this paper has a great noise reduction ability, so that the neural network model can still achieve a high fault diagnosis precision in noisy environments. In short, the approach in this paper is expected to play a role in the fault diagnosis and fault monitoring of different machines. Just like wind turbines, which produce noise during work, if the gearbox fails, it cannot be maintained in time solely based on worker experience. In the area of fault diagnosis in the future, we will continue to explore how to conduct fault monitoring and diagnosis of machinery in loud noise conditions so that deep learning can be better applied to the field of machinery. H.G. : Formal analysis, Investigation, Methodology, Writing--original draft and Writing--review and editing. T.X. : Formal analysis, Software, Validation and Writing--review and editing. R.L. : Formal analysis, Investigation, and Writing--original draft. C.C. : Funding acquisition, Methodology, Project administration, Formal analysis, and supervision. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Market Supervision and Administration Bureau Research Plan Project of Hebei Province under Grant 2023 ZD11 and in part by the Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province (Hebei University of Engineering) under Grant 202206. Not applicable. The data presented in this study are available in [github] at [URL: [[https://gitcode.com/cathysiyu/mechanical-datasets/tree/mater?utm_source=csdn_github_accelerafor&isLogin=1](https://gitcode.com/cathysiyu/mechanical-datasets/tree/mater?utm_source=csdn_github_accelerafor&isLogin=1)]([https://gitcode.com/cathysiyu/mechanical-datasets/tree/mater?utm_source=csdn_github_accelerafor&isLogin=1](https://gitcode.com/cathysiyu/mechanical-datasets/tree/mater?utm_source=csdn_github_accelerafor&isLogin=1))]. reference number [30].