## 3 Results and Discussion  
The last step of the algorithm is to combine the results of signal processing for different frequencies. For each frequency band, the value _true/false_ is returned describing whether there are repeating peaks observed on the autocorrelation of the signal. The effects of this process are presented in Figure 8, where the frequencies for which repeating peaks occurred in the autocorrelation are marked with dark blue. There were two damaged rollers on the inspected section of the belt conveyor. Two pronounced clusters of dark blue points are observed in Figure 8, which correspond to those damaged rollers. In addition, a higher density of points can be observed between those clusters, which also indicates damage to the conveyor element, but of a different nature. Accurate fault identification could not be performed in this case due to insufficient information in the recording. By aggregating these results (Equation (4)), we can obtain the percentage of frequency bands, for which repeating peaks in the autocorrelation signal are observed in time (\(W(t)\)),  
\[W(t)=\frac{1}{\max(\omega)}\sum_{n=1}^{\max(\omega)}\left(P_{n}\in(45,\,55) \right)\wedge\left(P_{n}\
otin(45,\,55)\right), \tag{4}\]  
where \(P_{n}\) represents the peak of the autocorrelation at a given frequency \(\omega\). Results of this operation are shown in Figure 9, where the blue line is the percentage of the number of frequency bands in which the change is detected, while the red line is the same value averaged. Two main failures are clearly visible on this plot. In addition, one can also see one smaller peak, which is caused by the unidentified damage to the conveyor. The analysis showed that the signal is influenced by many factors. However, none of them can raise its value above 30%, while in the case of the problem examined, the signal increases to the level of about 60-80%. Figure 8: Detection results over the entire signal length. Two visible failures have been marked with red ellipses. In order to test the presented algorithm, we decided to compare its results with other algorithms with similar applications. The first such algorithm was the one presented by [PERSON] [32]. This algorithm approaches the subject very similarly, first performing segmentation, then transforming the signal into the frequency domain in which it is filtered. From the signal processed in this way, the following features were selected: peaks exceeding the set limit, the total number of peaks in the segment, and the product of peaks amplitude in each window. These features were then processed by the SVM algorithm, but this operation was omitted due to the lack of an appropriate training sample. Another algorithm implemented for comparison is the one presented by [PERSON] [23], where the authors first calculated the feature vector from segments of signal (statistical measures) and then reduced its dimensionality with PCA and made classification using three algorithms (K-means, Spectral Clustering, Deep Neural Network). The comparison shows the classification on the sample using K-means, with the number of groups sought equal to 4. The last algorithm of the comparison is that described by [PERSON] [24]. Its authors focused on the calculation of four features on the segmented and filtered signal, two from the spectrum (kurtosis, skewness), and two from the autocorrelation (maximum, 95 th percentile). Additionally, a timeline was added to the comparison to better present what was happening in the sample. The comparison results are shown in Figure 10.  
As can be seen in the comparison, the proposed algorithm correctly detected type 1 two defects and partially one type 2 defect that remained unaffected by the disturbances in the sample. The type 1 defect at the end was also found by [PERSON] and [PERSON]; [PERSON] detected a type 2 defect. All three algorithms used for the comparison showed some degree of increase in the defect measure when the tripper car alarm appeared for the second time.