## 3 Results
### Artificial Intelligence as a Component of HMGT Systems  
Artificial intelligence plays a central and transformative role in the architecture of a health management system, especially within aviation. It infuses intelligence across various layers of the system (Figure 3), enhancing data analysis, decision-making processes, and operational efficiencies. AI integrates into each relevant layer of the architecture:  
* In the data collection layer, AI does not directly collect data; it influences the development and deployment of smart sensors and IoT devices. AI algorithms can preprocess data at the edge (close to where data are generated), filtering out noise and reducing the volume of data that needs to be transmitted and processed centrally. This selective data transmission improves efficiency and ensures that only relevant data are sent for further analysis. * In the data transmission and integration layer, AI plays a role in optimizing data transmission protocols and ensuring data integrity during transmission. It can dynamically adjust data transmission based on network availability and bandwidth, ensuring that critical data are prioritized. Furthermore, AI techniques can be used to integrate and synchronize data from disparate sources, resolving discrepancies and providing a unified data model. * In the data processing and storage layer, AI, particularly machine learning models, is employed to preprocess and clean the data, identifying and correcting errors, filling in missing values, and standardizing data formats. This layer uses AI to ensure that the data stored, whether in the cloud or locally, are of high quality and ready for analysis. * In the analysis and decision-support layer, AI is especially effective, offering several functionalities:  
* In predictive analytics, machine learning models analyze historical and real-time data to predict future system failures, wear and tear, and maintenance needs. * As diagnostic tools, AI algorithms diagnose issues by analyzing patterns and anomalies in the data, determining the root cause of system alerts and malfunctions. * As optimization algorithms, AI uses complex algorithms to optimize maintenance schedules, resource allocation, and operational planning, ensuring optimal outcomes based on a multitude of constraints and objectives. * In regulatory compliance modules, AI can automate the monitoring and reporting of compliance with aviation regulations, analyzing operational data against regulatory requirements and highlighting potential non-compliance issues. * In the actionable insights and interface layer, AI enhances user interfaces with intelligent alerts and recommendations, making complex data understandable and actionable for different users. It personalizes dashboards and reports based on user roles and preferences, ensuring that each user receives relevant and timely information. * In the feedback and continuous improvement layer, AI models continuously learn from new data and outcomes, improving their predictions and recommendations over time. This layer enables the system to adapt to changing conditions and evolve based on performance feedback, ensuring that the health management system becomes more accurate and effective. AI is integral to enhancing the capabilities of health management systems in aviation, providing the intelligence needed to transform raw data into actionable insights, optimize maintenance and operations, and ensure safety and regulatory compliance. Deploying AI onboard an aircraft for HMGT tasks presents both opportunities and challenges. While the ground segment of an HMGT system benefits from robust computational resources and comprehensive data access, integrating AI directly onboard introduces real-time processing capabilities that can enhance certain aspects of aircraft operation, safety, and maintenance:  
* AI can continuously monitor sensor data from critical aircraft systems (engines, avionics, hydraulics, etc.) in real time, instantly detecting anomalies or deviations from normal operational parameters.