## 5 Conclusions  
In this contribution, an investigation of the detection of various bearing damages is presented. Despite the measurement of the bearings in the compound of the two-stage planetary gearbox, which results in the superposition of vibration events, nonlinear transmission paths, low-speed components and noise, the results show that a reliable detection of the bearing damage is achievable. A distinction can be made between six presented damage phenomena. For this purpose, vibration signals are collected by using an accelerometer. The vibration signals are evaluated by creating spectrograms, which are used by CNNs as input data. In addition to the investigation of damage classification, complementary investigations regarding the robustness of the developed system are performed. These are intended to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Unknown Class** & **ACC\({}_{\text{mean}}\)** & **ACC\({}_{\text{max}}\)** & **ACC\({}_{\text{min}}\)** \\ \hline dist 0 & 0.96 & 0.99 & 0.90 \\ dist 1 & 0.98 & 1 & 0.97 \\ dist 2 & 1 & 1 & 1 \\ local 0 & 0.88 & 0.98 & 0.72 \\ local 1 & 0.98 & 1 & 0.97 \\ local 2 & 0.95 & 0.98 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classifier results for withheld classes with two subclasses. Figure 5: Confusion matrix for the classifier with three sub-classes tested on the withheld class _local 1_. test the robustness of the system for field application as well as the expected variance in the damage cases. The results show that the classifier is robust for variations in the speeds within the trained speed ranges. Also in most cases, the classifier can achieve high accuracies in classifying unknown damages in the presented three sub-classes. However, due to difficulties with individual damage phenomena, the use of a binary classifier, which differentiates only between healthy and damaged, is recommended, especially for high-risk applications with an expected variance of damage phenomena to occur. Since the classifier shows high robustness regarding unknown parameters, a transferability to planetary gearboxes with a different number of stages can be assumed. For this purpose, the network should probably be retrained. But due to missing access to equivalent data of a planetary gear with a different number of stages, a verification is not possible within this contribution. Therefore, the presented results are limited to the investigated two-stage planetary gearbox. For future work, the classifier should be tested and optimized for bearing damages in the two-stage planetary gearbox installed in vehicles. Since the presented classifier can detect unknown damage phenomena, good transferability is assumed. Since unknown parameters, like additional noise or the influence of the load on the characteristics of the damage phenomena, are introduced in the field application, an adaptation of the classifier and additional filters are probably needed. Conceptualization, J.S. ; methodology, J.S. ; software, J.S. and L.J.B. ; validation, J.S. and L.J.B. ; formal analysis, J.S. and L.J.B. ; investigation, J.S. ; resources, J.S. and K.-L.K. ; data curation, J.S. ; writing--original draft preparation, J.S. ; writing--review and editing, J.S., L.J.B., F.C. and K.-L.K. ; visualization, J.S. and L.J.B. ; supervision, K.-L.K. ; project administration, K.-L.K. ; funding acquisition, K.-L.K. All authors have read and agreed to the published version of the manuscript. This work is funded by the German Federal Ministry of Economic Affairs and Climate Action project KISS (19I21015D). The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: