# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
### Related Works
Another investigation compared different regression models, including random survival forest, to determine their performance in predicting aircraft engine failures, highlighting the potential of data-driven strategies in PM [18]. The concept of digital twins has been applied to enhance PM in aviation. A study demonstrated how RUL predictions, combined with digital twin models, can aid in designing stable supply chains and maintenance strategies, emphasizing the role of digital twins in predictive maintenance [19]  
The article of [20] presents a comprehensive systematic literature review of predictive maintenance in the aircraft industry, emphasizing its importance in optimizing maintenance schedules and reducing downtime. The review examines various data types, applications, and projects within the industry, aiming to identify and outline future research challenges and opportunities. Despite these advancements, challenges remain in implementing PM in aviation. Data quality and availability are significant obstacles, as noise in sensor data, missing data points, and inconsistent formats can impact ML model performance [21]. A comprehensive review [22] focuses on predictive maintenance within the defense aviation sector, emphasizing fixed-wing aircraft operations. The study employs bibliometric analysis and text clustering to identify key research areas, highlighting the integration of prognostics with decision support systems and identifying practical challenges in PM implementation. The study [23] introduces a unified ecosystem for data sharing and AI-driven predictive maintenance. This framework integrates real-time and historical data from diverse sources, including aircraft sensors and maintenance logs, to enhance decision-making processes for airlines and maintenance providers. The application of AI in PM is further explored in [24], which discusses key challenges and future directions for AI-driven predictive maintenance applications. The study emphasizes the importance of data quality, model interpretability, and integration with existing maintenance systems. The transition from traditional aircraft health monitoring to advanced health management approaches is examined, highlighting the role of the Internet of Things (IoT) and AI in enabling predictive maintenance [25]. The study discusses how real-time data collection and analysis can facilitate proactive maintenance strategies, enhancing flight safety and operational efficiency. The challenges associated with data-driven predictive aircraft maintenance are analyzed in [26], which identifies hazards related to the introduction of data-driven technologies into aircraft maintenance. The study underscores the need for improving the reliability of condition monitoring systems, ensuring timely communication between agents, and building stakeholders' trust in new technologies. The role of prognostics and health management (PHM) systems in aviation maintenance is discussed in [27], focusing on their diagnostic and prognostic capabilities. The study emphasizes the importance of PHM in maintaining aircraft integrity and reliability by forecasting and preventing malfunctions before they occur. A novel decision-making framework for the lifecycle management of aircraft components is presented in [28], integrating advanced data analytics, AI, and predictive maintenance strategies. The study introduces a data-driven approach for optimizing maintenance scheduling and resource allocation, aiming to enhance reliability and cost-effectiveness. Reference [29] explores the enhancement of predictive maintenance by analyzing the sensitivity of maintenance decisions to changes in model parameters such as costs and repair duration. An original approach is proposed that integrates maintenance costs and risks into a unified objective function to minimize, utilizing RUL as an indicator of system health.