## 5 Conclusions  
The results show that the selected PdM methods are meaningful in the EHAP. During the test series, they were able to contribute to a better assessment of wear behavior and estimate the condition of the axle. Despite the limitations that the test runs have high variability and that no exact damage cases could be defined in the data, such as a missing tooth on the bevel gear, the results are satisfactory. The OSVM is very reliable at finding anomalies. No anomaly remained undetected, so damage to the test bench and DUT could be responded to at an early stage. For the \begin{table}
\begin{tabular}{c c c} \hline
**DUT (N)** & **Pretrained Model Available** & **No Pretrained Model Available** \\ \hline
1 (468) & 0.86 s (126.19 s) & 60.15 s (214.60 s) \\
4 (177) & 0.78 s (68.44 s) & 89.04 s (223.86 s) \\
7 (394) & 0.94 s (109.05 s) & 81.75 s (199.57 s) \\
9 (362) & 0.95 s (119.78 s) & 92.82 s (188.21 s) \\ \hline \end{tabular}
\end{table} 
Table 6: Runtime calculation of the evaluation of the LSTM network for a model that has already been trained and without a pretrained model. \begin{table}
\begin{tabular}{c c c c} \hline
**DUT (N)** & **Validation Accuracy** & **Validation Loss** & **Value** \\ \hline
1 (468) & 99.82\% & 0.0213 & 1 \\
4 (177) & 99.81\% & 0.0292 & 14 \\
7 (394) & 99.02\% & 0.0339 & 2 \\
9 (362) & 99.98\% & 0.0009 & 0.3 \\ \hline \end{tabular}
\end{table} 
Table 7: Validation accuracy and loss of the trained LSTM networks of the associated tests. \begin{table}
\begin{tabular}{c c c c} \hline
**Features Used** & **DUT (N)** & **Pretrained Model Available** & **No Pretrained Model Available** \\ \hline \multirow{4}{*}{(a) circumferential} & 1 (468) & 6.46 s (1924.80 s) & 49.67 s (2006.87 s) \\  & 4 (177) & 5.44 s (839.58 s) & 56.29 s (901.95 s) \\  & 7 (394) & 5.69 s (1491.34 s) & 63.46 s (1615.78 s) \\  & 9 (362) & 6.32 s (1913.35 s) & 68.66 s (2060.19 s) \\ \hline \multirow{4}{*}{(b) local tooth damage} & 1 (468) & 5.9 s (2088.43 s) & 72.97 s (2356.98 s) \\  & 4 (177) & 5.72 s (833.16 s) & 57.97 s (890.54 s) \\ \cline{1-1}  & 7 (394) & 6.33 s (1549.91 s) & 67.01 s (1722.27 s) \\ \cline{1-1}  & 9 (362) & 6.6 s (1882.23 s) & 64.23 s (1981.91 s) \\ \hline \end{tabular}
\end{table} 
Table 5: Runtime calculation of the evaluation of the OSVM for a model that has already been trained and without a pretrained model. Features used for (a) circumferential and (b) local tooth damage. approach of specifically identifying a broken tooth, the ML method was redesigned to detect local tooth damage. In this design, the method can be used with both condition-based and statistical PdM. For circumferential tooth damage, initial signs of wear such as pitting, or even abnormal test bench components, were classified as abnormal using all seven features. However, when a pretrained model is used for circumferential tooth damage, it is not robust enough to be applied to similar DUTs. Another reason for this can be overfitting, which means that too many features have been used that negatively influence the result. In this case, it can only be used as condition-based PdM. The LSTM network is trained on a heavily worn differential, some of which have tooth damage, and reliably recognizes comparable conditions even in other DUTs. The selected deep learning method showed no reaction to the developing damage on the test bench. This suggests that it can distinguish the DUT well from the overall system. To detect the occurred damage to the test bench, it must be trained for these cases and their effects on the torque signal. With an outstanding performance of over 99%, the LSTM network solves the classification task between normal and abnormal data, as shown in Table 7. The performance of the ML and DL algorithms differs also in terms of calculation time, with various factors being considered. For existing trained models, a calculation time of approx. 7 s after a run is sufficiently fast to be able to intervene in the event of damage being detected.