DIRECT TO PHASE II: Edge-Deployed Explainable Digital-Twin Condition Based Maintenance CBM+ Platforms for Carrier-Based Systems

Carrier-based aviation platforms under NAVAIR generate massive, high-frequency streams of voltage, current, pressure, vibration, and temperature data across multiple subsystems—from the Advanced Arresting Gear (AAG) and steam catapults to hydraulic deck handlers and fuel-management valves—but Denied, Disrupted, Intermittent, Limited (DDIL) connectivity in carrier deployment forces maintenance crews to batch-download raw logs via physical media and ferry them ashore for analysis, introducing critical delays that can obscure early indicators of seal leakage, actuator fatigue, or control-valve drift, drive up unscheduled maintenance and lifecycle costs, and jeopardize sortie rates and aircrew safety in forward-deployed environments. DoDI 4151.22 mandates that CBM+ be “examined, evaluated, integrated, and incorporated” into weapon-system engineering and sustainment plans to optimize readiness and reduce life-cycle costs, and OPNAVINST 4790.16C requires Navy program managers to document CBM+ implementation and assess CBM+ maturity at every acquisition and sustainment review. By embedding a reusable, edge-deployed digital-twin core with semantic-AI reasoning, CBM+ workstations directly fulfill these requirements, enabling on-platform prognostics, formal CBM+ documentation, and continuous maturity assessments throughout NAVAIR system development and fielding. Topic Focus: The Navy seeks proposals for an edge-deployed (at the point of need, e.g., on the ship), explainable (i.e., Explainable Artificial Intelligence) Digital-Twin CBM+ Platform—hereafter “CBM+ Workstation”—that: 1. Ingests & Reduces High-Rate Sensor Streams Onboard. Leverage model-order-reduction and adaptive sampling to transform raw sensor traces (e.g., water-twister pressure, hydraulic fluid temperature, vibrations, noise, electric-motor power conditioning system sensors, etc.) into compact, information-rich features in real time into compact, high-value analysis that facilitates transmission under DDIL limitations. 2. Embeds Hybrid Physics & Semantic-AI Reasoning. Fuse first-principles Digital Twin models with maintenance-domain knowledge graphs and shipboard asset and contextual mission-specific knowledge graphs to automatically infer and explain emerging fault modes, delivering human-readable diagnostics rather than opaque alerts, and providing fault isolation to the Lowest Replaceable Unit (LRU). 3. Supports Plug-and-Play Workstation Generation. Provide a secure developer API to instantiate customer-specific CBM+ “workstations” for any carrier-based subsystem, enabling rapid customization and field upgrades without source-code changes. 4. Meets Shipboard Resilience Standards. Demonstrate performance on MIL-SPEC or ruggedized hardware, with <1 second anomaly-detection latency and <10 MB/hour data-transfer footprints to shore when intermittent links reconnect. 5. Provide integration via high-level APIs with shore-based analytics and logistics processes. By funding this topic, the Navy will catalyze a reusable, standards-based CBM+ solution family—accelerating deployment of predictive-maintenance workstations across the fleet, reducing unscheduled downtime, and ensuring mission readiness even in contested, communication-limited environments.