Dynamic matching of Aircraft Maintenance Capabilities and Tactical Needs using Machine Learning and Big-Data

Purpose and goal

The project has met the purpose and achieved expected level of results regarding concept feasability and technology maturity in central aspects for application of the researched technologies within the operation, maintenance and logistics support domain. The project results provide several possible utilizations in aviation and further research. The SoS approach, the modular and flexible properties of the concept and adapted technologies, also implies that the results are highly suitable for application in other domains, e.g. transport, mining and dynamic industrial system.

Expected results and effects

Project has resulted in a concept including a Graph-based data foundry and a lightweight and dynamic representation of relevant domain knowledge, that facilitates and interfaces an IVHM framework for integration of individual aircraft health-status updates in a generic maintenance support system. Elements that integrated enables a non-intrusive cross-domain information fusion, supports ML and BD technologies, and can leverage data- and relation-driven insights for decision support that in practice are unattainable in today’s system environments of stove-piped systems.

Planned approach and implementation

The project approached the matching-concept development for of tactical needs against maintenance support resources, through integrated work in six joint industry-academy cooperative work packages, including a concept demonstrator. Despite the impact by the pandemic on the projects internal and external activates and coop in the Cluster Operation and Maintenance, the project approach has produced an overall outcome according to expectations, albeit with a 4 months extension of the time plan.