## 4 Results
### Paderborn Bearing Dataset  
The Paderborn bearing dataset can also be seen as a benchmark for fault detection and condition monitoring of damaged rolling bearing elements of rotating equipment, cf. [20; 42]. The dataset represents motor current signals of an electromechanical drive system and vibrations of the housing [8]. The signals can be extracted in the existing frequency inverters. Therefore, no additional sensors needed to be placed on the system, as was the case in the CWRU bearing experiments, resulting in more resource-efficient experimentation, thus less expensive. Monitoring damages in external bearings, which are positioned in the drive system but outside the electric motor, is a unique feature of the current method. Regardless, the motor current signal was employed as an input for fault detection.  
In total, the data derived from the experiments represents \"healthy\", \"real damaged\" and \"artificially damaged\" bearings. Data were recorded for approximately 4 s with a sampling rate of 64 kHz, resulting in many separate data files with approximately 256 thousand data points. We concatenated the data files based on their specific bearing conditions, e.g., \"healthy\", \"real damaged\" and \"artificially damaged\" bearings. Due to the many different machine settings, we decided not to deviate between these, and only look at the specific bearing fault condition. This resulted in a large multi-class classification task and a large-scale dataset containing many sequences of length 2048, similar to the CWRU data.  
However, in contrast to the CWRU data, the number of classes is a bit lower, the data between every single class are more balanced and due to the high number of sequences, the training time of the models is much higher. Furthermore, the Paderborn Bearing dataset contains three time series, e.g., two motor current signals and one vibration signal, that are taken into account during processing. Therefore, the first wide-kernel layer adapts itself to  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experiment Name** & **Motor Speed** & **Nr. of Classes** & **Samples per Class** \\ \hline
12k Fan End & 1797 & 13 & 59 \\  & 1750 & 12 & 59 \\  & 1730 & 12 & 59 \\  & 1772 & 12 & 59 \\ \hline
12k Drive End & 1797 & 14 & 59 \\  & 1750 & 14 & 59 \\  & 1730 & 14 & 59 \\  & 1772 & 14 & 59 \\ \hline Normal Condition & 1797 & 1 & 119 \\  & 1750 & 1 & 236 \\  & 1730 & 1 & 237 \\  & 1772 & 1 & 236 \\ \hline \end{tabular}
\end{table}
Table 1: Number of data samples (sequences) for every experiment type, with their respective motor speed (RPM) and the supplementary normal conditions.  
the input data, resulting in a slightly changed architecture compared with our models on the CWRU data.  
For this experiment we also used \(k\)-fold cross validation with \(k=5\) to improve the generalizability of the trained models and tested models' performance with different train splits. However, due to the larger number of available samples, we attempted to complicate the classification task by lowering the train split even further, e.g., with only 10% and 5% training data. Table 2 provides an overview on the different conditions with their respective number of samples.