Abdullahi, IbrahimLongo, StefanoSamie, Muhammad2025-02-282025-02-282024-11-08Abdullahi I, Longo S, Samie M. (2024) An enabling architecture for computational cost efficiency in predictive maintenance digital twins. In: 2024 International Conference on Cyber-Physical Social Intelligence (ICCSI), 8-12 Nov 2024, Doha, Qatarhttps://doi.org/10.1109/iccsi62669.2024.10799418https://dspace.lib.cranfield.ac.uk/handle/1826/23534As digital twins emerge to provide a replication of physical assets in the digital space, the application of predictive maintenance of industrial asset becomes more effective. Developing digital twins for the predictive maintenance case study leverages Internet of Things, cloud computing and machine learning. While these technologies extend the necessary tools for deploying predictive maintenance digital twins, an enabling architecture facilitated by fog computing positions predictive maintenance digital twins for improved computational cost and latency than centralizing in the cloud or running locally at the edge. This work presents the application of a distributed digital framework, showing the benefits of better compute utilization and latency by adopting a distributed digital twin framework for predictive maintenance of wind turbine components in a wind farm.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/4606 Distributed Computing and Systems Software46 Information and Computing SciencesMachine Learning and Artificial Intelligence7 Affordable and Clean Energy9 Industry, Innovation and InfrastructureConference paper561716