# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 3 Results
### The Analytical IT System Framework as a Foundation for AloT in Aviation Health Monitoring  
Figure 3 illustrates an analytical IT system framework that forms the backbone of a modern aviation health monitoring system. This framework embodies the core principles of the AloT, integrating data collection, analyses, and decision-making processes to enhance aircraft reliability and operational efficiency. Figure 3: The analytical IT system framework for AloT. Figure 2: Taxonomy of sensors in an aircraft health monitoring system. At the heart of this system is AHMS with two critical data collection components:  
* WDAR--Wireless Digital AIDS Recorder (AIDS--Aircraft Integrated Data System [56]) unit [57];
* FMDE--Flight and Maintenance Data Exchange unit [58]. These elements represent the IoT aspect of AIoT, serving as the primary sources of real-time data from the aircraft's various systems and components. The AIDS is an essential component of modern aviation, gathering and integrating data from various aircraft systems to provide comprehensive monitoring, analyses, and control capabilities. By improving flight safety, enabling efficient maintenance practices, and facilitating data-driven performance optimization, the system has become a vital tool for airlines and aviation operators. The WDAR is likely designed to collect a wide range of flight data, including parameters related to aircraft performance, system status, and environmental conditions. The FMDE unit, on the other hand, focuses on gathering detailed maintenance-related information. Together, these units form a comprehensive data acquisition network that captures the complex operational state of the aircraft in real-time. The data collected by these units feed into the central analytical IT System, which acts as the brain of the framework. This system integrates data from multiple sources, including the airline's maintenance information system (MIS) and original equipment manufacturer (OEM) data [59]. By combining real-time operational data with historical maintenance records and manufacturer specifications, the analytical IT tools and applications create a rich, contextual dataset for analysis. The application of artificial intelligence within this framework is evident in three key outputs: predictive and prescriptive maintenance, health management, and enhanced reliability. These elements represent the AI component of AIoT, where machine learning algorithms analyze the integrated data to provide actionable insights and predictions. Predictive and prescriptive maintenance use historical data and real-time inputs to forecast potential equipment failures before they occur and prescribe optimal maintenance actions. This proactive approach allows airlines to schedule maintenance activities more efficiently, reducing unplanned downtime and extending the lifespan of aircraft components. Health management utilizes AI algorithms to continuously assess the condition of various aircraft systems. By analyzing patterns and anomalies in the data, this feature can detect subtle changes that may indicate emerging issues, allowing for early intervention and preventing minor problems from escalating into major failures. Enhanced reliability is achieved through the cumulative effects of predictive maintenance and health management. As the system learns from each maintenance action and flight, it refines its predictions and improves the overall reliability of the aircraft fleet. The framework culminates in the airline decision-making systems, where the insights generated by the analytical IT system are used to inform strategic and operational choices. This could include optimizing maintenance schedules, adjusting flight operations to maximize efficiency, or making long-term fleet management decisions. This analytical IT system framework exemplifies the potential of AIoT in aviation health monitoring.