Aircraft components are subject to numerous, complex and often manual maintenance, repair and overhaul (MRO) procedures to ensure long operating cycles. In order to remain competitive in the long term, in spite of increased cost pressure, MRO service providers must improve the efficiency of their processes through the targeted use of internal knowledge sources. Techniques from the fields of Artificial Intelligence (AI) and Data Mining (DM) have already proven their potential in diverse domains. However, the application of such data-driven approaches is also associated with some hurdles that need to be eliminated in advance. Data are generated at the business process level, known as Information Technology (IT, e.g. Enterprise Resource Planning (ERP) systems), as well as at the equipment level, known as Operational Technology (OT, e.g. test equipment). The integration of both forms the basis for improving the maintenance activities of diagnostics and maintenance scheduling. However, creating a unified view and understanding of the manifold data related to the maintenance process is a major problem due to the heterogeneous data sources and formats included. In this context, the use of Semantic Technologies (ST) can be helpful to overcome these challenges and provide the foundation for improved data management. The objective of this contribution is to introduce an ontology that delineates fundamental domain concepts, facilitating the augmentation of maintenance process data for individual aircraft components with pertinent contextual information. The result is being applied within the scope of a proof of concept aimed at supporting the coherent digital services diagnostics and short-term maintenance planning.


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    Title :

    Utilisation of semantic technologies for the realisation of data-driven process improvements in the maintenance, repair and overhaul of aircraft components


    Additional title:

    CEAS Aeronaut J


    Contributors:

    Published in:

    CEAS Aeronautical Journal ; 15 , 2 ; 459-480


    Publication date :

    2024-04-01


    Size :

    22 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English