## 4 Results
### One-Class Support Vector Machine (ML)  
The results of the OSVM presented are shown below. The empirical probability determined from Equation (17) is listed over the number N of runs of the target value profile driven. Runs 30-40 are used for training or for the specification of normally classified torque sequences. The basis for this is the check in Figure 6. The first runs are not well suited, due to run-in processes. This was checked using runs 0-10, 10-20, and 20-30. Later runs (>40) should not be used, as otherwise the first signs of wear could be taught in. In addition, a distinction was made in the anomaly detection of the OSVM between damage to a single tooth (local) and to all the teeth of the gear (circular). Furthermore, the trained models are also tested on other DUTs, i.e., the method is also used as a statistical PdM. The results of the inspection for circular tooth damage are shown below, followed by those for local tooth damage. In Figure 7, the OSVM results series shows that, for each test, a model for rotating gear damage, i.e., including all seven features, was trained and applied to the test in question. As already mentioned in Section 2.2, there were several incidents during the tests that are explained in connection with the results. Figure 6: Result of anomaly detection by the OSVM on the 1 st DUT for testing different time spans for learning normal measurement data. Figure 7: Result series of anomaly detection by the OSVM, considering all seven features. All tests were trained on their own model. First DUT (**a**), fourth DUT (**b**), seventh DUT (**c**), and ninth DUT (**d**). The result of the first DUT is shown in Figure 7a. The test ran normally and without any special incidents. From run 95 onwards, the OSVM begins to recognize an anomaly or a change in the specific features of the gearbox. From that point on, the empirical probability increases permanently. From run 215 onwards, the values are permanently 1, except for two outliers. However, the DUT did not fail until run number 465. This means that the method recognizes an anomaly at an early stage. Or, to put it another way, the anomaly that is recognized is an early sign of wear. The test of the fourth DUT in Figure 7b shows abnormal behavior at the beginning of the test, which quickly changes to a normal state. This may be due to changes in the gearbox caused by the running-in process. From run 50 onwards, the vibration values on the DUT increased. At this point, the side shafts of the differential had to be replaced due to abnormality. This meant that the empirical probability remained at 1 for the rest of the test. The test was terminated after the 177 th run. The differential showed heavy pitting and bearing damage. The replacement of the side shafts led to a change in the monitored features, and thus to abnormal behavior, which caused the anomaly detection to fail. Figure 7c shows the result for the seventh DUT, which behaves in a similar way to the first DUT. However, an anomaly appears in run 90, which initially disappears again. This can be seen from the steep increase in the empirical probability and the subsequent abrupt drop. From run 110 onwards, the value increases continuously and then remains at 1. In the context of the test procedure, the first anomaly detected is associated with impending breakage of a gearbox mounting of the DUT. To generate such a deflection, the resulting vibration must have been superimposed on the frequency of the gear-specific features. In run 103, this mounting was torn off and repaired. This resulted in elimination of the anomaly. Furthermore, wear in the differential is also identified as an anomaly here. The DUT failed from run 394. The inspection of the ninth differential in Figure 7d behaved similarly. From run 124, a kind of clustering occurs over 40 runs, after which the empirical probability breaks out and rises continuously until it stabilizes at 1 from run 185. Again, the wear was recognized as an anomaly. The DUT failed from run 362 and could no longer be operated.