## 3 Experimental Verification
### Description of the Dataset  
The planetary gearbox HFXZ-I dataset from the laboratory was applied to the study of fault diagnosis methods in this paper. The experimental platform is shown in Figure 5 and mainly consists of a variable speed drive motor, a coupling, a helical gearbox, a planetary gearbox, a magnetic powder brake, a variable speed drive controller and a load controller. The relevant base parameters are shown in Table 1. The data source selects the vibration signal measured by the acceleration sensor mounted on top of the planetary gearbox. The vibration signal was sampled continuously for one minute at three motor speeds (approx. 1200 r/min, 900 r/min, 600 r/min) and three loads (1 A, 0.5 A, 0.3 A) according to the 10,240 Hz sampling frequency.  
This experiment starts from the planetary gearbox system, which contains typical failures common inside a planetary gearbox, including gear pitting, gear wear, sun gear broken teeth, sun gear wear, gear cracks, inner race defects, and outer race defects. The details of the faults are shown in Figure 6. To further enhance the adaptability of the method to the degree of failure, the experiments were carried out for each of the different failure modes to further test the generality of the method as well as its ability to determine the level of failure. Experiments were performed thirty-six times, corresponding to the twelve types of failures under three load and speed operating conditions.  
Figure 4: Flow chart of methodology.  
Figure 5: Planetary gearbox experiment platform.  
Figure 6: Gears and bearings with different defects. (**a**) Gear pitting (**b**) Gear wear (**c**) Sun gear broken teeth (**d**) Gear crack (**e**) Inner race defect (**f**) Outer race defect (**g**) Sun gear wear.  
It is worth mentioning that this experimental dataset mixes single and compound faults and single faults of different fault levels, which can further demonstrate the decoupling capability of the method for faults and the generality of the extracted features. Another advantage that the model proposed in this method possesses is that the input data are variable in length. For this reason, data of different cut lengths are provided in the data pre-processing stage. The proportion of the training set is 30% of the total data. The details of the experimentally acquired data are shown in Table 2. The vibration signals corresponding to different pattern labels are shown in Figure 7. It should be emphasized that the signal amplifier multipliers in the experiments are different for different operating conditions, and thus the vibration acceleration values are in different ranges. Data is normalized when used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pattern Label** & **Gearbox** & **Rotating Speed (Hz) + Load (A)** & **Length of Samples** & **Training Ratio** \\ \hline C0 & Normal & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C1 & Gear pitting & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C2 & Gear crack & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C3 & Gear wear (level 1) & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C4 & Gear wear (level 2) & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C5 & Gear wear (level 3) & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C6 & Gun gear broken teeth (level 1) & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C7 & Gun gear broken teeth (level 2) & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C8 & Inner race defect & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C9 & Outer race defect & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C10 & Sun gear wear + C1 & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline C11 & Sun gear wear + C4 & 40 + 1/30 + 0.5/20 + 0.3 & 1024/2048/4096 & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details about the dataset.  
Figure 7: Gears and bearings with different defects.