## 3 Results
### Architecture of Aviation Health Management System  
The architecture of an HMGT system, especially in the context of aviation, is designed to handle vast amounts of data from diverse sources, process these data to extract actionable insights, and support decision-making processes that enhance aircraft performance and safety (Figure 3). There are some layers within this architecture:  
1. Data collection layer: * Sensors and IoT devices, which continuously monitor health and performance metrics such as temperature, pressure, vibration levels, and usage cycles. Each sensor is designed for specific components, from engines to hydraulic systems, ensuring comprehensive coverage. * Flight data recorders capture detailed flight operational data, including parameters related to flight controls, engine performance, and environmental conditions, which are crucial for both real-time monitoring and post-flight analysis. * Electronic maintenance logs digitally record all maintenance actions performed, providing a detailed history for each aircraft component. This includes information on part replacements, repairs, and routine inspections. 2. Data transmission and integration layer: * Communication networks, which utilize satellite communications for real-time data transmission during flight and ground-based networks for data offloading post-flight. This ensures that data collected in flight are rapidly available for analysis. * Data integration platforms aggregate data from multiple sources, including sensor data, FDR data, maintenance logs, and even external databases such as weather services or manufacturer databases. This integrated data platform is key to providing a holistic view of aircraft health. 3. Data processing and storage layer: * Data preprocessing tools address data quality issues, normalize data from different sources to a common scale, and fill in gaps in data records. This step is crucial to ensure the analytics engines can accurately interpret the data. * Cloud storage and local databases offer a flexible and scalable solution for storing processed data, with redundancy and disaster recovery capabilities. Local \begin{table}
\begin{tabular}{c c c} \hline \hline
**Service** & & **Sources of Information** \\ \hline Operational decision & - & Flight operations data: schedules, flight paths, and efficiency metrics. \\ support & - & Crew availability: staffing and crew qualification records. \\ \hline Safety analysis and risk & - & Incident and accident reports: analyses of past incidents and \\ management & - & Safety audit results: findings from internal and external safety audits. \\  & - & Regulatory safety directives: advisories and mandates from aviation \\  & - & Operational data analysis: trends and anomalies that indicate risks. \\ \hline Regulatory compliance monitoring & - & Regulatory databases: up-to-date requirements and changes in aviation \\  & - & Compliance checklists and tools: software that matches operational data \\  & - & against regulatory requirements. \\  & - & Audit reports: documentation from previous regulatory inspections \\  & & and audits. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._storage is used for storing sensitive or critical operational data, ensuring they are accessible even when cloud connectivity is not available. 4. Analysis and decision-support layer: * Predictive analytics engines employ advanced machine learning algorithms and statistical models to identify patterns and trends in the data that may indicate potential failures or maintenance needs before they become critical issues. * Diagnostic tools use algorithms to analyze symptoms and fault codes to pinpoint the underlying causes of observed anomalies, reducing diagnostic time and increasing accuracy. * Optimization algorithms apply techniques such as linear programming and genetic algorithms to find the optimal solutions for maintenance scheduling, resource allocation, and other operational challenges, balancing cost, performance, and regulatory compliance.