Assessment and Modelling of Joint Command and Control in Aircraft Maintenance Contexts Using Enterprise Models and Knowledge Graph Representations
2022 (English)In: International Journal of COMADEM, ISSN 1363-7681, Vol. 25, no 2, p. 13-22Article in journal (Refereed) Published
Abstract [en]
The increasingly complex context of dynamic, high-tempo military air operations raise new needs for aircraft maintenance and logistic support systems to more rapidly respond to changes in operational needs and available resources, with retained support resource efficiency. The future maintenance and support system are thus envisioned with improved net-centric capabilities to facilitate matching of tactical needs with aircraft maintenance capabilities. The study addresses this challenge by creating abstract representations and definitions of relevant tactical structures and maintenance structures, processes, and resources using enterprise modelling. By addressing this in a holistic perspective, a better understanding of the matching problem is achieved, enabling efficient matching of operational needs with available resources. Based on these findings, graph models are created from a domain-centric view of two adjacent domain contexts, which includes command and control and aircraft maintenance contexts. The result has the ability to leverage interoperability and collaboration between air- and ground-based systems by facilitating interactions between tactical needs and aircraft maintenance resources.
Place, publisher, year, edition, pages
COMADEM International , 2022. Vol. 25, no 2, p. 13-22
Keywords [en]
Aircraft Maintenance, Artificial Intelligence, Enterprise Modelling, System-of-Systems, Aircraft, Command and control systems, Maintenance, System of systems, Enterprise models, Graph representation, Joint command and controls, Knowledge graphs, Maintenance resources, Matchings, Operational needs, Tactical needs, Interoperability
National Category
Other Civil Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-64174Scopus ID: 2-s2.0-85168775604OAI: oai:DiVA.org:mdh-64174DiVA, id: diva2:1794855
2023-09-062023-09-062023-09-06Bibliographically approved