## 2 Basic Propositions and Research Objectives  
One way to increase the OEE value is to reduce the risk of component failures by using fault detection technology to unconditionally adhere to the planned transport schedule. The problem is complicated by the fact that each STT is a unique transport means, consisting of specialized motor vehicles and oversized semi-trailers capable of carrying unique AIL items that vary in both size and weight [15]. Transporting such loads involves a heightened risk of accidents. Therefore, the authors suggest that each STT should be equipped with a three-level threat prediction and STT capability monitoring system that operates in real-time and employs non-invasive technologies for monitoring vibrations and noise, with the following objectives (Figure 2):  
1. Identifying defects, manufacturing faults, and procedural breaches (e.g., load securing);
2. Detecting anomalies (both isolated and continuous) to prevent vehicle component failures;
3. Recognizing signs of cyberattacks on the diagnostic system within the CPS (Cyber Physical System) block;
4. Predicting catastrophic events in each STT component;  
Figure 1: Types of accidents recorded during AIL transport.  
Figure 2: Research focus.  
Detecting critical steering errors impacting the work schedule.  
For AIL transport, it is essential to continuously monitor the correct placement and securing of loads. Traditional methods that rely on identification codes to detect potential issues are not effective for AIL transport, as STTs face not only occasional but also surprising threats occurring due to the unique nature of super-heavy loads, asymmetric center of gravity positions as a result of shape asymmetry, and bending/tipping moments with cascading effects. Figure 3 shows the three blocks of the Monitoring and Diagnosis System concept. The components of each are illustrated.  
The Cyber-Physical System block contains STT components, sensors and controllers, and microprocessors. The Forecasting, Monitoring, and Diagnosis block contains three knowledge bases, which, in turn, are responsible for the following: reading the values of working process parameters (set of characteristics of routine threats), detection and analysis of anomalous values (set of nominal values for operating parameters), and discovering new knowledge (collection of template recommendations). The expert block includes the techniques and methods needed to be used in the whole process described. The information coming from these three blocks is being provided in a visualized form to the driver (Figure 3).  
The above analysis underscores the critical need for ongoing diagnosis, detection, and mapping of both potential and existing faults, along with research to mitigate their risk. This need is driven by the rapid advancement of IT technologies, which facilitate real-time monitoring of key parameters and the transmission of crucial information. This paper introduces a proposed three-level model encompassing application, control, and communication layers, along with a system for monitoring, diagnosing, and predicting threats. Both occasional and surprising threats were analyzed, considering the risk of losing the STT's transport capability.  
The purpose of the study was to determine whether providing truck drivers with information from an active threat warning system could help reduce unplanned downtimes. It also sought to develop an integrated platform for monitoring, diagnosing, and mapping faults to inform the driver about the STT's operational status, with threat probability assessments and preventive measures displayed visually on a 3D monitor.  
Figure 3: Concept of distributed monitoring and diagnosis system architecture for STT using IoT (Internet of Things) techniques and related services.