## 3 Results and Discussion
### Classification of Stationary Conditions  
The classification is executed under the influence of artificial damage and the operating conditions, as described in Table 1. The confusion matrix illustrates the accuracy of the damage classification along the primary diagonals, with an increase in damage severity with each damage level. Figure 6 presents the confusion matrices for both the five-fold cross-validation and the repetition split. The five-fold cross-validation demonstrates near-perfect accuracy, reaching 100% in the confusion matrix (see Figure 6a). However, a closer examination reveals deviations, which are also present in the _new_ class. These deviations can be attributed to variations in the raw signals of the gear wheel sensor [15]. In contrast, the repetition split exhibits significantly lower accuracy, with an accuracy of 89% (see Figure 6b). This lower accuracy is also evident in the confusion matrix. This is attributable to the fact that the gearbox adapter is disassembled and reassembled during the various repetitions, resulting in a modified vibration pattern. Nevertheless, the false predictions are concentrated around the main diagonal, suggesting a similarity between the different damage levels as they progressively increase in terms of damage size. It is noteworthy that the _New_, _1T_, and _3T_ classes each possess data from all four pinions, while the remaining classes are equipped with data from only two pinions. This discrepancy partially explains the significant deviation observed in the _3T_-2x120\({}^{\circ}\) class.  
Following the classification with the gear wheel sensor, the repetition split classification with the pole ring sensor and the acceleration sensor is compared (see Figure 7). The pole ring sensor demonstrates the highest prediction accuracy at 97%, followed by the acceleration sensor at 92%, and the gear wheel sensor exhibits the lowest at 89%. For the pole ring sensor, false predictions are distributed around the main diagonal, once more reflecting the similarity in the damage levels (see Figure 7b). A significant deviation was identified at class _3T_-3x120\({}^{\circ}\) for the acceleration sensor, which was classified as new and  
Figure 6: Confusion matrices for classification results at stationary gear wheel sensor conditions with (**a**) five-fold cross-validation (accuracy: 100%); and (**b**) repetition split (accuracy: 89%), according to [16].  
false at 25%. This strong deviation is not present in the other sensors and demonstrates the acceleration sensor's susceptibility to incorrect predictions due to its more complex transfer path. This is primarily attributable to the attachment of the accelerometer to the gearbox adapter, which is removed between the different repetitions. Notwithstanding this deviation, the sensor demonstrates a high degree of prediction accuracy. With a more substantial dataset and enhanced classification algorithms, the impact of these errors could be mitigated.  
These findings indicate that a high degree of classification accuracy can be attained with the gear wheel sensor. Nevertheless, the pole ring sensor yields superior outcomes, which is primarily attributable to tooth-specific fluctuations in the angle signal when measuring with the gear wheel. It is essential to acknowledge that these outcomes necessitate validation through further research with augmented data and supplementary classification algorithms to facilitate a comprehensive evaluation of the gear wheel sensor.  
Figure 7: Confusion matrices for classification results at stationary conditions with repetition split: (**a**) gear wheel sensor, 89% accuracy; (**b**) pole ring sensor, 97% accuracy; (**c**) acceleration sensor, 92% accuracy, according to [16].