# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 5 Experiment and Analysis
### Noise Reduction Performance Analysis  
With the aim of conveniently measuring the efficiency improvement of the model after noise diminishment, Gaussian white noise with a certain signal-to-noise ratio (SNR) is added to the original vibration signal. The calculation formula of SNR is as follows  
\[SNR_{dB}=10\log_{10}(P_{signal}/P_{noise}) \tag{20}\]  
where \(P_{signal}\) is the power of the original vibration signal, and \(P_{noise}\) is the power of the added Gaussian white noise. Figure 6 shows the comparison result between the original signal of the gearbox tooth broken under 7.32 Nm load and the noised signal with SNR = 0. As seen in the figure, after increasing the noise, the fault characteristics in the signal are interfered with to different degrees, and the fault features are not apparent. Hence, noise lessening is needed for the noisy signals to upgrade the discernment precision of the model for the fault signals containing a lot of noise. The noise reduction approach presented in this paper was adopted for noise reduction. Firstly, the ICEEMDAN algorithm was used to decompose the noised signal, and the result is presented in Figure 7. As can be found from the figure, after ICEEMDAN decomposition, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **0.72 Nm** & **0 Nm** \\ \hline TICNN & 99\% & 99.2\% \\ GASF+SE-ResNeXt18 & 99.5\% & 99.6\% \\ GASF+MobileNetV3 & 99.7\% & 99.8\% \\ MTF+TSE-ResNeXt50 & 99.8\% & 100\% \\ GASF+TSE-ResNeXt50 & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of Different Models on GASF gearbox-Based Datasets. Figure 6: Comparison result between the original signal and the noised signal. eight IMF components and one residual component are obtained, respectively. ICEEMDAN successfully decomposed the noised fault signal into high-, medium- and low-frequency components, and each component has different time scales, without obvious mode aliasing. Then, the average MPE of each IMF component was calculated, which is presented in Table 5. As depicted in the table, the average value of IMF1-IMF4 is greater than 0.6, so the adaptive wavelet threshold noise reduction is needed. IMF components with an average MPE greater than 0.6 were first denoised by using AWT, and then the denoised IMF components were reconstructed with the undenoised IMF components. Specifically, the denoised IMF1-IMF5 components were reconstructed with IMF6-IMF8 and the residual component, resulting in a denoised signal. Figure 8 illustrates the comparison result compared to the denoised signal and the noised signal. It can be seen that the amplitude of the noised signal is \(-\)0.1\(-\)0.1; after the noise reduction approach raised in this paper, the amplitude is reduced to \(-\)0.05\(-\)0.05. Moreover, it can be found from the figure that the signal after noise reduction has more significant fault \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**s** & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** & **IMF8** & **Res** \\ \hline
1 & 0.834 & 0.734 & 0.497 & 0.338 & 0.245 & 0.189 & 0.158 & 0.127 & 0.108 \\
2 & 0.865 & 0.849 & 0.722 & 0.480 & 0.340 & 0.249 & 0.185 & 0.141 & 0.114 \\
3 & 0.837 & 0.787 & 0.789 & 0.591 & 0.415 & 0.304 & 0.213 & 0.152 & 0.120 \\
4 & 0.787 & 0.795 & 0.768 & 0.682 & 0.470 & 0.348 & 0.241 & 0.169 & 0.126 \\
5 & 0.777 & 0.779 & 0.697 & 0.725 & 0.518 & 0.387 & 0.263 & 0.186 & 0.131 \\
6 & 0.738 & 0.763 & 0.716 & 0.732 & 0.553 & 0.434 & 0.287 & 0.192 & 0.137 \\
7 & 0.728 & 0.736 & 0.697 & 0.732 & 0.572 & 0.463 & 0.310 & 0.211 & 0.142 \\
8 & 0.709 & 0.707 & 0.711 & 0.698 & 0.598 & 0.471 & 0.335 & 0.217 & 0.146 \\
9 & 0.702 & 0.687 & 0.692 & 0.689 & 0.627 & 0.504 & 0.331 & 0.227 & 0.151 \\
10 & 0.674 & 0.680 & 0.677 & 0.669 & 0.643 & 0.500 & 0.368 & 0.246 & 0.156 \\
11 & 0.651 & 0.658 & 0.658 & 0.640 & 0.652 & 0.503 & 0.369 & 0.254 & 0.161 \\ AVG & 0.755 & 0.743 & 0.693 & 0.634 & 0.512 & 0.396 & 0.278 & 0.193 & 0.136 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MPE of each IMF component.