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Award details
- Awarding agency
- Department of Defense
- Branch
- Air Force
- Recipient company
- WILLIAMSRDM, INC.
- Proposal award date
- 2013-01-31
- Contract end date
- 2013-10-30
- UEI
- Z28BFD9MEYL8
- Solicitation number
- 2012.1
- Topic code
- AF121-225
Research abstract
ABSTRACT: The Air Force"s High Velocity Maintenance (HVM) initiative aims to better emulate industry"s high daily rate of touch-labor maintenance and rapid return-to-service. One barrier to HVM is scheduling of the maintenance. Currently, once an aircraft arrives at the depot, it spends time in the maintenance queue waiting for the problem to be identified, which requires a crew debrief and manually downloading on-board fault identification data. Once the faulty subsystem is identified, the aircraft can then be routed to the appropriate repair station. Williams Pyro proposes a system that will constantly monitor the aircraft"s systems, store pertinent information, and allow for a quick, error free transfer to the service depot"s computer. The transfer will be done over a secure wireless connection when the aircraft is in range of the system"s wireless access points. Software running on the depot"s computer system will efficiently store current flight data and recommend maintenance or repairs. The software will also analyze historical data from multiple aircraft stored in its database and make suggestions on preventative maintenance and on part ordering, specifically parts with long lead times. The wireless communication, server software, and data mining algorithms all make up Williams-Pyro"s Maintenance and Repair Aircraft Diagnostic System (MaRADS). BENEFIT: The benefit to the MARADS system is that maintenance information is transmitted to the depot prior to the aircraft arriving. This allows the maintenance debrief to start earlier than the current method. Hangers can be assigned and people and the tools can be routed to the proper area. This speeds up the time the aircraft gets to the hanger and also minimizes down time of resources (people and tools). A less obvious benefit is the Maintenance and Repair Learning Algorithm (MaRLA) that takes historical data from all connected depot servers and determines preventative maintenance schedules, trends, and recommends repair and maintenance parts in anticipation of long lead time for delivery. This benefit allows the aircraft to remain in service for longer durations, to have shorter downtimes for maintenance, and to possibly have a longer lifespan. All these benefits lead to lowering the cost of total aircraft ownership. Potential commercial applications include: - The 250 existing C-130J aircraft plus 2000 other C-130 variants, with approximately 50% converted to the"J"spec. - Maintenance data collection and trending for the L-130J civilian version of the military C-130J. - Maintenance data collection and trending for the other commercial airline applications.
Technology keywords
Not reported
Other awards by this recipient
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Awards with the same topic code
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Research funding history
71 SBIR/STTR records share this UEI (1999–2014). Reported award amounts total $15,259,111. Reported amounts are not federal obligations.
Phases: Phase I, Phase II
Agencies: Department of Defense, Department of Commerce, Department of Homeland Security, National Aeronautics and Space Administration, National Science Foundation, Department of Energy
Agency intelligence
Department of Defense
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