# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 4 Experimental Validation and Analysis
### Case II  
(1) Gear datasets: To further verify the effectiveness of the proposed method, different fault types in the gear datasets are collected and used for validation. As shown in Figure 8, there are six different damaged types of gears.  
As is shown in Table 3, the gear datasets contain six different fault types, i.e., healthy gear, 1-tooth worn gear, 2-tooth worn gear, 2 mm crack gear, 3 mm crack gear, and 4 mm crack gear. The latter three damages are different crack lengths in the tooth root radial direction. The datasets were collected at a motor speed of 3120 rpm with 2 V (Torque: 4.06 N\(\cdot\)m) load.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Model** & **Min – acc (\%)** & **Max – acc (\%)** & **Avg – acc (\%)** & **Avg- Time (s)** \\ \hline \multirow{6}{*}{Rolling bearing datasets} & CNN & 73.95 & 74.34 & 74.23 \(\pm\) 0.1 & 274 \\  & MSC-MpResCNN & 78.17 & 79.26 & 78.81 \(\pm\) 0.32 & 562 \\  & ARSN-M & 96.73 & 97.42 & 97.00 \(\pm\) 0.23 & 1729 \\  & DRSN-CW & 97.53 & 98.28 & 97.91 \(\pm\) 0.22 & 1426 \\  & MK-ResCNN & 97.87 & 98.29 & 98.10 \(\pm\) 0.13 & 1823 \\  & AMRS & 98.34 & 99.2 & 98.78 \(\pm\) 0.30 & 1802 \\  & AMC-RSN & 99.08 & 99.43 & 99.24 \(\pm\) 0.11 & 1922 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fault diagnosis results of rolling bearing.  
Figure 8: Gears with different failure modes. (**a**) 1-tooth worn. (**b**) 2-tooth worn. (**c**) 2 mm crack. (**d**) 3 mm crack. (**e**) 4 mm crack. (**f**) Healthy.  
Figure 7: These three pictures show the front, back, and side of the test rolling bearing respectively.  
(2) Analysis of Fault Diagnosis Results in Gear Datasets: As is shown in Figure 9, the proposed AMC-RSN has the best diagnostic effect and the lowest diagnostic accuracy of CNN compared with other methods. From Table 4, it is noteworthy that the proposed AMC-RSN can obtain the highest accuracy of 98.24% with a standard deviation of 0.16%, which reflects that AMC-RSN has good robustness. Compared with DRSN-CW, MK-ResCNN, and MSC-MpResCNN, the proposed AMC-RSN has significantly improved its ability in extracting the features of the original signal with the highest diagnosis accuracy. Besides, the AMC-RSN has superior performance when compared with the AMRS and ARSN-M.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Model** & **Min – acc (\%)** & **Max – acc (\%)** & **Avg – acc (\%)** & **Avg- Time (s)** \\ \hline \multirow{6}{*}{Gear datasets} & CNN & 67.59 & 68.78 & 68.24 \(\pm\) 0.39 & 186 \\  & MSC-MpResCNN & 81.88 & 82.8 & 82.46 \(\pm\) 0.31 & 434 \\ \cline{1-1}  & ARSN-M & 93.5 & 94.19 & 93.85 \(\pm\) 0.21 & 1377 \\ \cline{1-1}  & DRSN-CW & 95.87 & 96.41 & 96.15 \(\pm\) 0.15 & 1074 \\ \cline{1-1}  & MK-ResCNN & 95.79 & 96.06 & 95.92 \(\pm\) 0.10 & 1412 \\ \cline{1-1}  & AMRS & 96.41 & 97.02 & 96.81 \(\pm\) 0.18 & 1422 \\ \cline{1-1}  & AMC-RSN & 97.78 & 98.24 & 98.06 \(\pm\) 0.16 & 1521 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fault diagnosis results in gear.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Fault Type** & **Training Set** & **Test Set** \\ \hline \multirow{6}{*}{3120 rpm-2V} & Healthy & 868 & 218 \\  & 1-tooth worn & 868 & 218 \\  & 2-tooth worn & 868 & 218 \\ \cline{1-1}  & 2 mm crack & 868 & 218 \\ \cline{1-1}  & 3 mm crack & 868 & 218 \\ \cline{1-1}  & 4 mm crack & 868 & 218 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the gear datasets.  
Figure 9: Diagnostic results of different models for the gear datasets.