Democratized Learning Enabling Multi-Level Digital Twin Model Integration

Effective exploitation of Machine Learning solutions in the Digital Twin (DT) paradigm may largely benefit from Federated Analytics (FA) approaches, to mitigate data scarcity, by merging distributed data, and heterogeneity while limiting communication overhead and exchange of sensitive raw data. In the DT paradigm, federated schemes find a native collocation in the conceptual association between Digital Twin Prototype (DTP) of a class and Digital Twin Instance (DTI) of individual products. We propose the application of the democratized learning (Dem-AI) scheme to provide a scalable solution for multi-level hierarchical integration of data owned by a multiplicity of distributed DTs, and we showcase its application in a failure prediction scenario. The proposed model integration scheme preserves the inherent cohesive relationships between generalization and specialization (or personalization) capabilities of the DTP model and the DTI model, respectively. Based on the model acquired, DTs monitor the system behavior and forecast failure occurrences. Experimental analysis has been conducted to thoroughly investigate the performance of the Dem-AI failure prediction framework designed, considering different levels of model specialization and public dataset.