Recognition Framework for Efficient In-context Performance Measurement (REVOICE)

Digital Twin technology has the potential to close the gap that currently exists in the life-cycle management loop.  The operations goal is application and integration of appropriate processes, technologies, and knowledge-based capabilities to improve performance, reliability, and maintenance effectiveness.  By synchronizing real-time asset operation, physics based virtual models, and logistics intelligence the operator has a complete cradle to grave view of an asset’s design, current state, and predicted state under a wide range of operating load conditions.    BluEyeQ’s Phase I research objective is to close the mission readiness loop between the physical and virtual world via a Digital Twin platform using data from all available entities.  For success we focus our efforts on a scalable platform approach that accurately predicts and models life-cycle operation across a broad component mix.  This approach features innovative Computational Intelligence that monitors machine vital signs and diagnoses health based on measured and predicted performance parameters.  We build off our existing industrial solution and demonstrate feasibility on active industrial gearboxes.