# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 5 Experiment and Analysis
### Performance Analysis of Different Noise Reduction Methods  
For the sake of validating the superiority of the noise reduction approach suggested in the current paper, a comparative experimental study was conducted by using ICEEMDAN-MPE-AWT, CEEMDAN-MPE-AWT, ICEEMDAN-WHT (ICEEMDAN with Wavelet Hard Thresholding) and ICEEMDAN-MPE-WHT. Among the above denoising methods, CEEMDAN-WHT used the cross-correlation coefficient between intrinsic mode functions to distinguish whether noise reduction is required for this component. The dataset with a load of 7.32 Nm was selected as the experimental dataset. Since GASF+TSE-ResNeXt50 has achieved a good effect before and after noise reduction, to reflect the effectiveness of the noise reduction approach introduced in this paper, GASF+TSE-ResNeXt18 was selected as the test model. The recognition accuracy of the model on the test set was taken as the appraisal index, 10 repeated tests were carried out for each model, and the average value of the 10 tests was taken as the ultimate result. The experimental outcomes are demonstrated in Table 7 and Figure 11. It can be found that ICEEMDAN-MPE-AWT and CEEMDAN-MPE-AWT denoising methods achieved high accuracy; when SNR is 6 dB, both denoising methods can ensure an accuracy of over 99%, and as the SNR decreases, the accuracy decreases less. In addition, ICEEMDAN-MPE-AWT still has an accuracy of 99% when SNR is 2 dB, and the accuracy is higher than that of CEEMDAN-MPE-AWT. This shows that ICEEMDAN can better extract the local features and periodic components in the signal than that of CEEMDAN, it acquires the effective information of the signal and obtains more stable and reliable IMF components. Therefore, the combination of ICEMDAN with MPE and AWT has achieved better noise reduction effects and improved the accuracy of the model. Figure 10: The confusion matrices of GASF+TSE-ResNeXt50 before and after denoising when SNR is \(-4\) dB and load is 7.32 Nm. Figure 9: GASF+TSE-ResNeXt50 and GASF+TSE-ResNeXt18 models before and after noise reduction. Compared with CEEMDAN-MPE-AWT, the accuracy of CEEMDAN-MPE-WHT decreased significantly; this indicates that setting wavelet coefficients below the threshold to zero in WHT may give rise to the loss of elaborate information in the signal. Especially for some weak detail signals or low amplitude variation signals, WHT may not be able to effectively retain this detailed information. In addition, WHT needs to manually select the appropriate threshold for signal denoising. However, in practical applications, especially when the statistical characteristics of the signal are not clear or the noise level changes greatly, it is often very difficult to determine a suitable threshold value. Therefore, the accuracy of CEEMDAN-MPE-WHT has decreased to some extent. The AWT used in this paper can adjust the threshold dynamically by introducing a correction factor, and the calculation results of the threshold can be further adjusted by introducing a convergence factor \(\theta=k/L\), thereby improving the resolution of low-frequency signals and enhancing the denoising effect. This method can dynamically adjust the threshold value according to the local characteristics of the signal, which can better keep the details and edge information of the signal, has strong adaptability, and improves the stability and accuracy of denoising. Compared with CEEMDAN-MPE-WHT, CEEMDAN-WHT has similar accuracy when the SNR is high. However, when the SNR decreases, CEEMDAN-WHT fluctuates significantly. This indicates that the cross-correlation coefficient may not be able to accurately distinguish the noise component from the signal component, and it may be easy to divide the low-frequency component into noise, resulting in certain fluctuations in accuracy. In contrast, MPE can reveal the multiscale structure of a signal by calculating the arrangement entropy at different scales, which allows it to consider both local and global characteristics of the signal, thus, providing more comprehensive signal analysis results.