The MEGA2 project, funded by the German Federal Ministry of Economic Affairs and Climate Action (grant 20X1903A) and carried out from 1 July 2020 to 30 June 2022, aimed to create a manufacturer‑ and operator‑independent data platform for maintenance, repair and overhaul (MRO) of unmanned aerial vehicles (UAVs). The effort was led by Lufthansa Technik (LHT) in partnership with the German Aerospace Center (DLR) and supported by internal experts from Lufthansa Industry Solutions and a UAV developer. LHT was responsible for the platform architecture and data integration, while DLR supplied predictive‑maintenance models and evaluated their applicability to UAVs.
In the first work package (HAP 1) the team identified key use cases for a universal MRO data platform. Three major scenarios emerged: passenger transport, urgent cargo delivery, and surveillance—including potential military applications. Each scenario requires complex payloads, regulatory compliance, standardised data exchange, and scheduled MRO activities. The analysis revealed that UAVs differ markedly from manned aircraft in size, propulsion, and mission profiles, which complicates the transfer of existing predictive‑maintenance predictors. Nevertheless, the study confirmed that relevant information can be extracted from unstructured and incomplete data sources using artificial‑intelligence tools, providing valuable insights for maintenance planning.
The second work package (HAP 2) focused on the data‑platform concept. LHT examined existing platforms such as AVIATAR, which offers predictive‑maintenance services independent of the original equipment manufacturer. A comparison with Airbus and Boeing solutions highlighted differences in functionality and orientation. The platform must act as a digital twin of the UAV, capturing flight‑data telemetry, component health metrics, and maintenance documentation. A critical finding was that without an integrated enterprise‑resource‑planning (ERP) system linking aircraft operators and MRO service providers, data gaps arise when maintenance is performed by third‑party contractors. Moreover, the digital twin must provide automated scheduling of maintenance actions, a capability that current platforms only partially deliver.
The project demonstrated that a high‑performance data platform can satisfy the competing requirements of data volume, real‑time analytics, and interoperability. By leveraging AI‑based data extraction and predictive models, the platform can support proactive maintenance decisions, reduce downtime, and improve the reliability of autonomous UAV operations. While quantitative performance metrics were not reported, the qualitative assessment indicates that the platform meets the essential criteria for a universal MRO solution.
Collaboration across the consortium was structured around clear roles: LHT led the platform development and data integration, DLR focused on predictive‑maintenance algorithms and their validation, and Lufthansa Industry Solutions contributed domain expertise in UAV operations and regulatory compliance. The project timeline spanned two years, aligning with the funding period set by the ministry. The partnership model combined industrial experience with academic research, ensuring that the demonstrator platform could be scaled to support future autonomous aerial systems such as drones and personal aerial vehicles.