## 4 Fundamentals of the Developed Concept
### An Anomaly Detection Based on the DBSCAN Algorithm  
The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method is orchestrated to extract points from three categories:  
1. Baseline corresponds to normative values of measurable parameters. Located at a threshold distance from the main reference point. 2. Borderline. Located at threshold distances between neighboring baseline points and assessed by the Local Outlier Factors (LOFs) indicator. 3. Outliers correspond to above-normal values of measurable parameters. Located at an off-threshold distance from the main benchmark point. The execution process of the DBSCAN algorithm is depicted in Figure 6. The algorithm DBSCAN. Clarification. The developed algorithm provides an opportunity to discover new knowledge about the reliability of processes occurring in the trajectory of AIL transports by repeatedly testing the relevance of the developed concept with a description of the possible consequences of its application. It is based on the assumption that changes in the cluster density of the measured parameter values are random and that multiple measurements of these values contribute to a clear difference in these densities. Two supporting parameters were required to perform the ba-d study: \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Types of Anomalies**} & \multicolumn{2}{c}{**Characteristics of Anomalies in Selected**} \\  & **Operating Parameters of Monitored Vehicle** & **Features of Operating Parameter Anomalies** \\  & **Components** & \\ \hline Steady uptrend/downtrend & Gradual changes in anomalous values of operating parameters without abrupt changes. & \\ \hline Unsteady uptrend/downtrend & Regular (sinusoidal) or abrupt changes in anomalous values of operating parameters. & \\ \hline \hline \end{tabular} Source: Own elaboration based on [54].  
\end{table} 
Table 8: Characteristics of anomalies in selected operating parameters for monitored STT components for continuous change anomalies. * EPS--defines the neighborhood radius around the x-point under study. It is called its \(\varepsilon\)--neighborhood. * MinPts--defines the minimum number of neighbors within the \"EPS\" radius. Consequently, the probability of detecting clusters of supernormal values of measurable parameters/generating an input set of anomalous points for the developed algorithm increases with time. The detected anomalous points are then sorted in ascending order and mapped to create a message to the driver (Figure 7). Figure 6: Flowchart of the steps involved in solving a anomaly mining task. Advantages of the DBSCAN algorithm used in the study:  
1. It is oriented to detect three-dimensional clusters of measurable parameter values of any shape;
2. It is adapted to detect both baseline and anomalous points;
3. It does not require the assumption of patterned shapes of the sought clusters into the algorithm;
4. It has an above-average speed for detecting points with anomalous values;
5. During the analysis of clustering results, the shapes of the examined clusters are not distorted;
6. If necessary, there is a possibility to take into account exceeding the measurable values of parameters, i.e., to extract, i.e., \"anomalous points\", which, in our case, are the points of interest because they indicate non-normative values of measurable parameters of both individual CTT systems and their structural elements. An abnormal point in the time series corresponds to an abnormal parameter value indicating the occurrence or high probability of a fault event. The fragment of the experiment results is shown in Figure 8.  
Figure 8: The vehicle operating parameter clustering based on DBSCAN Me. Figure 7: Przykald komunikatu graficznego wysylanego kierowcy cigniku siodowego. The final component of the concept of monitoring the condition of STT as a whole proposed in this paper involves diagnosing both existing and potential faults and providing relevant information to the driver.