DON26BZ05-DV087 TITLE: DIRECT TO PHASE II: Edge-Deployed Explainable Digital-Twin Condition Based Maintenance CBM+ Platforms for Carrier-Based Systems
OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Artificial Intelligence (AAI)
COMPONENT TECHNOLOGY PRIORITY AREA(S): Advanced Computing and Software
PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)
OBJECTIVE: Develop Digital Twin modeling and simulation, leveraging existing and new sensors to detect faults and predict possible failures in Aircraft Launch and Recovery Equipment (ALRE) to provide advance notice and prepare "maintainers" performing Condition Based Maintenance (CBM).
DESCRIPTION: 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.
PHASE I: For a Direct to Phase II topic, the Government expects that the small business will have accomplished the following in a Phase I-type effort and developed a concept for a workable prototype or design to address, at a minimum, the basic requirements of the stated objective above. The following actions would be required to satisfy the requirements of Phase I:
Established the feasibility of the edge-deployed, explainable CBM+ Workstation by focusing on a representative NAVAIR subsystem, such as the Advanced Arresting Gear (AAG).
FEASIBILITY DOCUMENTATION: Offerors interested in participating in Direct to Phase II must include in their response to this topic Phase I feasibility documentation that substantiates the scientific and technical merit and Phase I feasibility described in Phase I above has been met (i.e., the small business must have performed Phase I-type research and development related to the topic NOT solely based on work performed under prior or ongoing federally funded SBIR/STTR work) and describe the potential commercialization applications. The documentation provided must validate that the proposer has completed development of technology as stated in Phase I above.
PHASE II: Develop a prototype CBM+ workstation core that operates on representative edge-computer hardware and processes live data from an AAG test site. Demonstrate semantic-AI fault inference for at least two critical AAG failure modes (such as seal leakage or valve blockage), generating confidence metrics and natural-language explanations to decrease maintenance downtime and improve AAG availability. Utilize existing sensor systems to perform electrical and mechanical diagnostics and prognostics. Create a software interface capable of automatically generating a lightweight CBM+ workstation for a second carrier subsystem, like the catapult accumulator system.
PHASE III DUAL USE APPLICATIONS: Transition the CBM+ Workstation from a prototype to a fully operational, supported, and scalable solution for the MK15 Advanced Arresting Gear and the Navy, while also pursuing broader commercial applications.
Commercial or industrial sites with limited network connectivity could greatly benefit from an edge-deployed digital twin for a variety of systems (additive manufacturing, HVAC systems, etc.).
REFERENCES:
KEYWORDS: Diagnostics; Prognostics; Health management; Digital twin; CBM+; Edge-compute
TPOC 1 : Matthew Marko
(732) 323-5228
matthew.marko.civ@us.navy.milTPOC 2 : Glenn Shevach
(732) 323-2602
glenn.m.shevach.civ@us.navy.mil
** TOPIC NOTICE ** |
The Navy Topic above is an "unofficial" copy from the Navy Topics in the DoW FY-26 Release 5 SBIR BAA. Please see the official DoW Topic website at www.dodsbirsttr.mil/submissions/solicitation-documents/active-solicitations for any updates. The DoW issued its Navy FY-26 Release 5 SBIR Topics pre-release on August 5, 2026 which opens to receive proposals on August 26, 2026, and closes September 23, 2026 (12:00pm ET). Direct Contact with Topic Authors: During the pre-release period (August 5, through August 25, 2026) proposing firms have an opportunity to directly contact the Technical Point of Contact (TPOC) to ask technical questions about the specific BAA topic. The TPOC contact information is listed in each topic description. Once DoW begins accepting proposals on August 26, 2026 no further direct contact between proposers and topic authors is allowed unless the Topic Author is responding to a question submitted during the Pre-release period. DoD On-line Q&A System: After the pre-release period, until September 9, 2026, at 12:00 PM ET, proposers may submit written questions through the DoW On-line Topic Q&A at https://www.dodsbirsttr.mil/submissions/login/ by logging in and following instructions. In the Topic Q&A system, the questioner and respondent remain anonymous but all questions and answers are posted for general viewing.
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