## 4 Fundamentals of the Developed Concept
### Structure of the Early Fault and Anomaly Detection System as a Cyber-Physical System  
To fulfill the outlined requirements, the in-vehicle monitoring system should comprise the following components:  
CDIF Classifier Module (Classifier to Defect and Identify Faults). This component monitors, detects, and identifies faults using statistical data (History-Based Fault Diagnosis), measurements (Hardware-Based Fault Diagnosis), and modeling outcomes (Model-Based Fault Diagnosis) [57]. DIPCA Module (Dynamic Inner Principal Component Analysis). Task: monitors the condition of vehicle components and analyzes changes in conditions over time [58]. Experience has indicated that the EF&AD system should include the following additional modules:  
1. Module for Mapping Faults and Abnormal Surfaces. During transport operations, identifying vehicle faults is part of the driver's responsibilities, typically supported by fault codes or indicator lights on the control panel, according to Tables 7 and 8. 2. Knowledge Fusion for the Deep Multi-Learning Module. This module extracts valuable explicit information recorded by sensors. In AIL transport, it is also crucial to detect hidden information by evaluating the likelihood of operational disturbances from rare faults (e.g., issues in the exhaust system) and the consequent multiplicative effects (e.g., decreased engine performance). It analyzes areas of anomalous parameter values and integrates data from various sensors to predict potential faults. 3. Preventive recommendation module. It is useful in cases with a high likelihood of faults, providing recommendations to the driver: * Routine--for occasional situations, it suggests actions such as changing driving modes or reducing speed to prevent fault propagation in vehicles--PFPV. * Non-routine--for surprising scenarios, like a steering system failure, it might suggest actions such as attempting to restart the engine, unlocking the steering by turning the ignition key, and engaging the brakes simultaneously. \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 \multirow{2}{*}{Bias} & Constant deviation of measured values. & \\  & Measurements indicate a shift by a constant value & \\  & from nominal values. & \\ \hline \multirow{2}{*}{Drift} & Gradual deviations in measured values. & They occur sporadically in ROF (Randomly \\  & Measurements indicate a shift over time. & Occurring Fault) mode. \\ \hline \multirow{2}{*}{Freezing} & Lack of response of measurement results to & They do not follow any rules \\  & changes in the dynamics of monitored parameters. & and are typically caused by wear, corrosion, or \\ \hline \multirow{2}{*}{Noise} & Random fluctuations in measurement results & \\  & unrelated to changes in the dynamics of monitored & \\ \multicolumn{2}{c}{parameters.} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Characteristics of anomalies in selected operating parameters for monitored STT components for isolated anomalies. Continuous anomalies are detected based on the process capability index \(C_{p}\), calculated as follows:  
\[C_{p}=\frac{\text{USL}-\text{LSL}}{6\sigma} \tag{1}\]  
where \(\sigma\) is the standard deviation calculated from the data obtained from the process. Isolated anomalies are detected using the DBSCAN (method based on the evaluation of values of selected operating parameters and their classification). The recommended procedures in this regard include the following [59; 60]:  
* Collecting data on the values of the operating parameters of individual vehicle systems;
* Performing a detailed analysis of the collected data and its clustering;
* Detecting anomalies and predicting potential short-term changes.