# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 4 Discussion
### Traditional Health Monitoring Model  
The traditional model of aviation health monitoring is characterized by its relative simplicity and linear approach. At its core, this model focuses on three primary components: system state, maintenance actions, and failure probability. The system state at any given time is typically assessed through periodic inspections or basic sensor readings. This state information, combined with records of previous maintenance actions, feeds into a failure probability function. This function, often based on statistical models or empirical data, attempts to predict the likelihood of system failure. \begin{table}
\begin{tabular}{c c c} \hline \hline
**AIoT Platform Stakeholders** & \multicolumn{2}{c}{**Benefits for Stakeholders**} \\ \hline Aircraft Manufacturers & \(\bullet\) & Access to real-world performance data to inform future designs. \\  & \(\bullet\) & Ability to monitor and analyze the long-term behavior of aircraft in diverse operating conditions. \\  & \(\bullet\) & Opportunity to provide more accurate and timely support to operators. \\ \hline Airlines & \(\bullet\) & Real-time health monitoring for optimized operations and maintenance scheduling. \\  & \(\bullet\) & Enhanced ability to predict and prevent potential disruptions. \\  & \(\bullet\) & Improved fuel efficiency through data-driven optimizations. \\ \hline MRO providers & \(\bullet\) & Shift towards more efficient, proactive maintenance strategies. \\  & \(\bullet\) & Ability to prepare for maintenance events with greater accuracy and foresight. \\  & \(\bullet\) & Potential for remote diagnostics and guided repair procedures using augmented reality. \\ \hline Airports & \(\bullet\) & Improved ground operations through better prediction of aircraft needs. \\  & \(\bullet\) & Enhanced resource allocation based on real-time and predicted aircraft statuses. \\  & \(\bullet\) & Potential for more efficient gate management and turnaround times. \\ \hline ATC & \(\bullet\) & Enhanced safety through real-time aircraft health data. \\  & \(\bullet\) & Improved efficiency in air traffic management based on more accurate aircraft performance data. \\  & \(\bullet\) & Potential for more dynamic and adaptive routing based on real-time conditions. \\ \hline OEM suppliers & \(\bullet\) & Direct access to performance data of their components in real-world conditions. \\  & \(\bullet\) & Ability to refine and improve products based on actual usage patterns and stress factors. \\  & \(\bullet\) & Opportunity for more accurate lifecycle predictions and warranty management. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Benefits of AIoT platform for key stakeholders. Maintenance decisions in the traditional model are primarily driven by this failure probability, along with cost considerations. A decision function takes these inputs and determines when and what maintenance actions should be taken. This approach often results in scheduled maintenance based on fixed intervals or simple threshold-based rules. The cost function in the traditional model is straightforward, typically calculating expenses based on the maintenance actions performed and the probability of failure. This model, while functional, lacks the sophistication to account for the myriads of factors that can influence aircraft health and performance in real-world operations. Traditional models of health management can be described as follows. Let us define the following variables: \(S(t)\)--system state at time \(t\); \(M(t)\)--maintenance actions at time \(t\); \(F(t)\)--failure probability at time \(t\); \(C(t)\)--cost function at time \(t\). The traditional model can be described as  
\[F(t)=f\{S(t),\;M(t-1)\} \tag{1}\]  
where \(f\) is a function that predicts failure probability based on current system state and previous maintenance actions. \[M(t)=g\{F(t),\;C(t)\} \tag{2}\]  
where \(g\) is a decision function that determines maintenance actions based on failure probability and cost considerations.