CHARLES RIVER ANALYTICS, INC. — Department of Defense SBIR Phase I: N192-124

Description

Due to changes in operational conditions, Navy systems maintainers are being asked to maintain increasingly complex systems with fewer maintainers and less oversight from master maintainers. As a part of Sailor 2025, the Navy is looking for systems that can provide “Ready, Relevant Learning” beyond the schoolhouse, providing continuous and on-the-job training and support systems that enable learning while doing. To address this challenge, we propose to design and demonstrate Readiness-Enhancing Virtual, Adaptive Maintenance Platform (REVAMP), an adaptive on-the-job performance support and training system using Digital Twin technology to increase the effectiveness and efficiency of maintenance repairs, saving time, improving confidence, and reducing errors. REVAMP features: (1) a hybrid-computational architecture to create digital twins that accurately model the systems to be maintained; (2) computational models of maintenance procedures to optimize maintenance recommendations; (3) an adaptive maintenance engine to perform automated analysis of the digital twin, assist troubleshooting, and suggest diagnostic and maintenance actions; (4) a role- and task-adaptive augmented reality intelligent user-interface (AR-IUI) to flexibly present maintenance recommendations and information to the human maintainer and overlay indicators on the actual equipment during maintenance using an AR headset; and (5) logging and secure sharing of maintenance issues and courses of action.