# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
## 2 Materials and Methods
### Conceptual Framework for Study  
The conceptual framework for this study is designed to provide a structured approach to evaluating the feasibility of PM implementation for aircraft components. This framework is built around three core components: identification of relevant criteria, development of a scoring and weighting system, and application of an aggregated decision-making model to assess PM suitability (Figure 2).  
\begin{table}
\begin{tabular}{c c c} \hline
**Technique** & **Applications** & **Tools** \\ \hline Vibration Analysis & Rotating equipment, motors & Accelerometers, vibration sensors, \\  & & data collectors \\ \hline Infrared Thermography & Electrical panels, heat exchangers & Infrared cameras, thermal imaging systems \\ \hline Ultrasound Testing & Compressed air systems, valves & Ultrasonic detectors, acoustic cameras \\ \hline Oil Analysis & Hydraulic systems, engines & Spectrometers, particle counters \\ \hline Machine Learning Algorithms & Predictive analytics & IoT sensors, AI-driven \\  & & maintenance platforms \\ \hline SCADA Systems & Centralized monitoring & Distributed sensors with real-time \\  & & data dashboards \\ \hline \end{tabular}
\end{table}
Table 1: Tools for predictive monitoring.  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Applicability of P-F Curve** & **Reason** \\ \hline Engines (Turbines) & High & Condition monitoring (e.g., vibration, thermography) detects degradation over time. \\ \hline Landing Gear & High & Wear and fatigue can be monitored with condition-based tools (e.g., crack detection). \\ \hline Hydraulic Systems & Medium & Degradation (e.g., fluid contamination) is gradual and measurable, but sudden failures \\  & & can occur. \\ \hline Avionics (Electronics) & Low & Failures are often sudden and not preceded by detectable degradation. \\ \hline Structural Components & High (for fatigue-related failures) & Cracks and corrosion follow predictable degradation patterns, detectable through inspections. \\ \hline Fuel Systems & Medium & Degradation (e.g., clogging, leaks) can \\  & Medium & sometimes be gradual, but unexpected events \\  & & also occur. \\ \hline \end{tabular}
\end{table}
Table 2: Aircraft components and the P-F curve.  
At its foundation, the framework identifies the key criteria influencing PM implementation. These factors are grouped into thematic categories to facilitate a structured analysis. Each criterion is quantified using measurable metrics, such as reliability indices for technical factors, cost estimates for economic considerations, and compliance scores for regulatory adherence.  
The second component of the framework involves defining a weighting system to reflect the relative importance of each criterion. Weighting is determined using robust methods such as AHP. This ensures that the framework accounts for varying priorities, such as placing greater emphasis on safety-critical factors or aligning with organizational goals. A scoring system is also developed to evaluate each component's alignment with the defined criteria on a standardized scale, ensuring consistency in assessment.  
The final component of the framework is an aggregation model that integrates the weighted criteria and scores to calculate a predictive maintenance suitability score for each aircraft component. This score is derived using the MCDA approach. The resulting score allows for a clear and transparent decision on whether PM is suitable for a specific component.  
This conceptual framework provides a rigorous methodology for integrating diverse factors into a unified decision-making process. By systematically evaluating components against well-defined criteria, the framework ensures a comprehensive and data-driven approach to predictive maintenance feasibility, ultimately supporting safer, more efficient, and cost-effective aviation maintenance practices.