## 4 Fundamentals of the Developed Concept  
### Concept of Distributed Condition Monitoring System Architecture  
One approach to improving this metric involves mitigating the risk of failure in STTs by utilizing technologies to detect condition-related threats and ensuring adherence to the planned transport schedule. The problem is complicated by the fact that each STT is unique, consisting of specialized motor vehicles and oversized semi-trailers capable of carrying unique AILs [15]. There is a pressing need to develop a comprehensive concept for monitoring the technical condition of the STT as a whole, including the ability to predict both occasional and surprising local threats during the transport of AILs. Experience indicates that even minor faults can potentially lead to significant multiplicative effects. Addressing these issues requires the development of an integrated, self-learning system capable of predicting, detecting, and mapping faults, which should include the following functions:  
1. Gathering and displaying on a 3D monitor real-time information about the condition of each component of the STT. This function should adhere to the APOP (All-Parts-in-One Platform) principle. 2. Visualizing existing and potential faults based on risk levels. This includes identifying dangerous symptoms and comparing them to nominal values. This function is grounded in the ABFDA (Anomaly-Based Fault Detection Approach). 3. Informing the driver about the likelihood of imminent failures. This should be carried out using the KMA (Knowledge Mining Approach), which aids drivers in decision-making processes. The EF&AD (Early Fault and Anomaly Detection) system designed to achieve these objectives should:  
1. Provide a comprehensive overview of the STT condition on-line, with the capability to drill down from subsystems to separate components of the vehicle, trailer, or cargo, while considering the vehicle's operational regime. The EF&AD should carry out the following tasks:  
1. Mechanical fault detection. In the network of sensors, converters, and controllers. Detecting physical degradation or damage to parts or components. 2. Total functional failure detection. Identifying unacceptable deviations in process parameters from normal values, like engine failure to start [54]. 3. Partial functional failure detection. Identifying acceptable deviations in process parameters, such as reduced engine performance, vibrations from components, sudden accelerations or braking, and oil leaks. Symptoms could include diminished braking efficiency or increased fuel consumption. 4. Electrical failure detection--typically involving the battery, alternator, connectors, fuses, and relays. Possible causes include alternator bearing failure, blocked starter solenoid, damaged insulation, or corroded connections. 5. Developing and providing visual prompts to assist drivers in managing the abnormal vehicle effectively. 6. Exhibit the following characteristics:  
1. Openness to interoperability by exchanging information with the sensor network and responding to signals received regardless of the vehicle's configuration. 2. Adaptability to various scenarios, including those influenced by initial conditions and random factors. Therefore, assessing the current state of the STT should be based on both the analysis of the distribution of operational parameter values for occasional threats and the analysis of anomalous value sets for surprising threats. 3. Functional activity capable of self-organization (e.g., optimizing sensor network efficiency by promptly addressing faults), self-regulation (e.g., adjusting information provided to the driver in response to abrupt changes in the parametersof operating processes), and self-improvement (e.g., continuously enhancing performance). * Synergy is achieved through supporting the interoperability of sensor networks that provide condition information, controllers that regulate component functions, and CPU microprocessors. This interoperability improves the system's overall effectiveness.