# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 1 Introduction
### Related Work
It further enhances the digital twin by automatically generating descriptive data and semantics, presenting a concept that not only preserves but also utilizes these data to significantly improve planning phases and retrofit processes. [PERSON] et al. [28] introduced a comprehensive framework for monitoring, diagnostics, and health management across an aircraft fleet, utilizing a signature-based algorithm. The digital twin of the engine was used to replicate fault signatures for key components. Although the study primarily focused on a physics-based approach, the authors briefly explored the potential of incorporating neural network classifiers to enhance the system further. [PERSON] et al. [29] concentrated on the turbofan disk, a critical component prone to crack failures. Their approach involved creating a digital surrogate of the disk usingmathematical models and dynamic equations, employing a fully physics-based method that analyzed vibration response signals to detect cracks. [PERSON] et al. [30] emphasized the importance of integrating and fusing heterogeneous data sources to develop a life prediction method for aircraft structures. They incorporated material properties and structural geometry into a digital twin, constructed using finite element methods, specifically to simulate crack propagation. [PERSON] et al. [31] addressed the environmental control system of an aircraft, deploying a model-based solution directly on a real aircraft during ground testing to demonstrate the practical applicability of their work. [PERSON] et al. [32] focused on health monitoring methods for aircraft electrical power systems. The digital twin of this network, integrated with other aircraft subsystems, was based on mathematical principles and well-established simulation tools. The data-driven aspect was represented by an artificial neural network developed for fault isolation and root cause prediction. In a follow-up paper [33], the authors introduced the FAVER framework, which uses digital twin concepts and reasoning techniques to identify, isolate, and predict faults across interacting aircraft subsystems. These use cases were demonstrated, showcasing a digital twin approach that combines both physics-based and data-driven modeling. This framework was further elaborated in a separate article [34], where three use cases were discussed, two of which relied on simulation models, while the third involved hardware-in-the-loop testing for more efficient results. The solutions applied in these studies used the open system architecture for condition-based monitoring, which enabled data manipulation algorithms and defined fault classification as part of the state detection and health assessment process. [PERSON] et al. [35] explored aircraft landing gear using an existing electric braking system model as the digital twin. By employing a physics-based approach within a software environment, they simulated specific fault types and incorporated a recurrent neural network to accurately identify these failures. [PERSON] et al. [36] offered a different approach to information fusion between physics-based and data-driven models, utilizing feed-forward and recurrent neural networks to develop an improved digital twin for real-time fault detection in aeroengines, specifically by constructing a degradation-adaptive correction model. [PERSON] et al. [37] addressed the challenge of aircraft airspeed estimation in the event of a pitot tube sensor failure, proposing a solution that combined physics-based simulation models with data fusion and estimation techniques. [PERSON] et al. [38] focused on the aeroengine, conducting practical tests in a test facility to validate their approach. The paper in [39] introduces a comprehensive framework for integrating digital twins into aircraft lifecycle management, utilizing advanced technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing.