## 4 Results
### CWRU Bearing Dataset  
The CWRU bearing dataset represents a bearing fault experiment where damages were applied to the bearing element. To measure vibration signals, sensors were placed on the drive end and fan end of the machine; we refer to [54; 55] for more detail. The test rig is also displayed at: [[https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)]([https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures](https://engineering.case.edu/bearingdatacenter/apparatus-and-procedures)); Figure 3 shows an example of the according signals. These vibrations are digitized into two time series that we segmented into sequences of 2048 data points without overlap. The data from the experiments used in this study is divided into two categories; 12k fan end and 12k drive end, both utilizing a sampling rate of 12 kHz. Both experiments contain different properties, resulting in deviating datasets with fluctuating amounts of classes. Within these datasets, different machine operations are used to measure the vibrations, respectively; 1797, 1750, 1730 and 1772 motor speed (revolutions per minute, RPM). Next to that, there is a normal condition for every motor speed.  
Overall, in many fault detection and condition monitoring studies, the CWRU bearing dataset [54; 55] has been used, e.g., [10; 43; 45; 56; 57; 58]. It can be considered as a de facto benchmark dataset for fault detection because it is publicly available and in principle modeled analogously to important industrial application settings, where data are typically not made openly available. Furthermore, the CWRU dataset is straightforward to interpret and analyze by combining the large number of supplied class labels, which are typically difficult for standard machine learning models to handle.  
There are three different depths of damages inflicted to the bearing, 0.007, 0.014 and 0.021 inches, respectively. Each damage type has five distinct bearing fault locations (ball, inner race, outer race opposite, outer race orthogonal and outer race centered). To enhance the tasks' complexity and restrict the number of data samples per class, we opted to count each condition as a single class. Thus, resulting in a large-scale classification task where distinctions are made on fault level and machine condition. The data samples are made by segmenting the two time series into 2048-point sequences with no overlap. This sequence length has been extensively utilized in other research for efficient implementation of the fast Fourier transform (FFT) method, which is known as a powerful baseline [10; 45; 59]. Every fault condition has approximately the same number of samples per class. Except for the normal conditions, where the number of samples fluctuate. An overview of the dataset with its number of classes and samples can be seen in Table 1.  
Figure 3: Example signal of the vibrations from both sensors derived from the CWRU Bearing dataset. The red box indicates the length of one data sample (sequence).  
Within these experiments, we applied varying combinations of the different motor speeds. Combining these different motor speeds leads to various numbers of samples, but also changes in the number of classes to classify, therefore increasing the complexity of the given classification task. In addition, we experimented with different percentages of training data, e.g., 80%, 40% and 20% training data, to exhibit the behavior of our model under different circumstances. Furthermore, we used \(k\)-fold cross validation with \(k\) set to 5 to improve the generalizability of the trained models. The classification accuracy of the models is calculated by averaging the accuracy across every fold. However, within every fold, we predicted on the same unseen test set. We chose accuracy as the most important metric due to the multi-class classification task with mostly balanced data (except the normal conditions), as used in many previous studies [10; 43; 45].