## 3 Results
### Artificial Intelligence as a Component of HMGT Systems
* Once an anomaly is detected, onboard AI can quickly diagnose potential issues, identifying their nature and severity. This immediate diagnostic capability can be crucial for in-flight decision-making and safety. * By analyzing data trends directly onboard, AI can predict potential failures or maintenance needs before they occur, even without real-time communication with ground systems. This is particularly useful for long-haul flights or operations in remote areas with limited connectivity. * Based on predictive analysis, onboard AI can suggest optimal maintenance actions, potentially advising the crew to adjust operations to mitigate risks until maintenance can be performed. It can also pre-notify ground maintenance teams about upcoming needs. * AI can monitor flight systems and environmental conditions to enhance safety, providing pilots with real-time insights into any emerging issues and suggesting corrective actions. * In critical situations, onboard AI can assist in assessing the situation, providing recommendations for emergency procedures based on the most current system health data and operational parameters. * AI algorithms can analyze flight data in real time to optimize fuel consumption, advising pilots on adjustments to speed, altitude, or routing that could save fuel without compromising safety or schedule. * Onboard AI can adjust flight plans in response to changing weather conditions, air traffic, or aircraft performance issues, optimizing for efficiency and safety. There are some problems with AI integration in onboard systems:
* Onboard systems have limited space, power, and cooling capabilities, which can restrict the computational resources available for AI algorithms. Solutions must be designed to operate within these constraints. * Introducing AI into critical flight systems raises security and safety concerns. Systems must be designed to be fail-safe, with robust security measures to prevent unauthorized access or interference. * Any onboard AI system must comply with aviation regulatory standards, which may require extensive testing and certification processes to ensure safety and reliability. All AI-driven analytics, including predictive maintenance, fault diagnosis, and decision support, are conducted on ground-based servers, where computational resources are less constrained. This setup allows for the use of more sophisticated AI models and analytics tools that may be too resource-intensive for onboard systems. The ground system can integrate data not just from the aircraft in question but from the entire fleet, as well as external data sources like weather services, to enhance the accuracy and efficacy of AI analyses. Aircraft are equipped with systems capable of transmitting real-time data from onboard sensors to the ground-based AI via satellite or air-to-ground communication networks. This ensures that the AI has access to up-to-date information for its analyses. Pilots and onboard systems have the capability to receive real-time insights, recommendations, and alerts from the ground-based AI, effectively making the AI's functionality available onboard through communication links. Transferring the functionality of onboard AI systems to a ground-based component, while maintaining real-time communication with this \"quasi-onboard\" AI from the aircraft, is an additional strategy. This approach leverages the best of both worlds: the computational power and scalability of ground-based systems and the real-time data collection and communication capabilities of onboard systems. The above-mentioned \"quasi-onboard\" strategy has both benefits and limitations:Functionalities and benefits:
* Even with the AI processing occurring on the ground, aircraft can benefit from real-time predictive maintenance insights and fault diagnosis. Recommendations and critical alerts can be communicated back to the aircraft for immediate action.