## 4 Results
### One-Class Support Vector Machine (ML)
It is interesting to note in the context of the test that an abnormal bellows on the drive had to be replaced after run 165. After the replacement, the clustering ended. Here too, the PdM provided an indication of abnormal behavior of the test bench, which had a slight influence on the extracted features of the DUT. Figure 8 shows the results of the OSVM when a model for circumferential gear damage was trained using the first test. This model is then applied to the data from the other tests. The results of the first test in Figures 7a and 8a are therefore identical. No good results are generated for the other tests in Figure 8b-d. Although the values are in the correct range towards the end of the tests, the behavior of the DUTs in the normal state appears to differ. This leads to anomalies being identified at the beginning. In some cases, these disappear again due to the factors mentioned, such as running-in processes or repairs of abnormal components on the test bench. Figure 9 shows the results of the OSVM, where a model for local gear damage, i.e., in terms of the features \(f_{x}\pm f_{2}\) and \(f_{x}\pm 2f_{2}\), was trained for each test and applied to the test in question. The results in Figure 9 differ in principle from those of the circumferential tooth damage, as they only increase at a later stage. This is in line with the theory of tooth damage. The empirical probability in Figure 9a increases gradually from run 150 onwards, as in the previous test, because wear can also affect the side bands, but not to the same extent as local damage. The value then increases steadily until it reaches 1 for the first time from run 405 onwards. After that, the empirical probability remains in this range until the end of the test. The test of the fourth DUT in Figure 9b again shows a strong run-in process, but then normal operating behavior for a long time. The change of the side shafts in run 50 has no influence on the monitored features of the local tooth damage. After 125 runs, the empirical probability then begins to rise and reaches a value of 1 after 25 more runs. The reason for the failure is not fully clear, but a combination of heavy pitting and bearing damage in the differential was observed. The result of the OSVM indicates abnormal behavior regarding the monitored features at the end of the test. The result in Figure 9c shows the same behavior as in Figure 9a. It is noticeable that the anomaly described in Figure 7c, where a mounting has come loose, is not recognizable. The external influences therefore had a greater effect on the tooth engagement frequency, which was not taken into account here. In contrast, examination of the data from the ninth test in Figure 9d shows similar clustering, as previously described in Figure 7d. An abnormal bellows stimulated the system in such a way that abnormal behavior was registered. In addition, the way in which the empirical probability develops towards the end is different. In comparison to the results of the first and seventh tests, which show a slowing increase,  
Figure 8: Result series of the anomaly detection of the OSVM, considering all seven features. All tests were taught on the model of the first DUT. First DUT (**a**), fourth DUT (**b**), seventh DUT (**c**), and ninth DUT (**d**). Figure 9: Result series of the anomaly detection of the OSVM considering the features \(f_{x}\pm f_{2}\) and \(f_{x}\pm 2f_{2}\). All tests were trained on their own model. First DUT (**a**), fourth DUT (**b**), seventh DUT (**c**), and ninth DUT (**d**). the progression here from run 350 onwards is more like exponential growth. Only the last eight runs have a value around 1. The OSVM results in Figure 10 show the use of the model trained on the first test run, in relation to the features \(f_{2}\pm f_{2}\) and \(f_{2}\pm 2f_{2}\), which was applied to the other tests. The graphs in Figures 9a and 10a are therefore identical. While the model trained on the fourth test in Figure 9b already indicates an anomaly at run 150, the empirical probability in Figure 10b is still 0 at run 150.