SBIR PHASE II, ENTITLED: REAL-TIME ADAPTIVE ALGORITHMS FOR FLIGHT CONTROL DIAGNOSTICS AND PROGNOSTICS. TECH…
Award details are derived from public USAspending.gov records. Not a bid recommendation.
Award details
- Award ID
- NNL08AA06C
- Contractor
- Awarding agency
- National Aeronautics and Space Administration
- Sub-agency / office
- National Aeronautics and Space Administration
- Award amount
- $0
- Period of performance
- Apr 16, 2008 – Nov 29, 2009
- NAICS
- 541712 — Research and Development in the Physical, Engineering, and Life Sciences
- PSC
- PSC AR11
- Place of performance
- VA
Public award description
SBIR PHASE II, ENTITLED: REAL-TIME ADAPTIVE ALGORITHMS FOR FLIGHT CONTROL DIAGNOSTICS AND PROGNOSTICS. TECHNICAL ABSTRACT: THE OVERALL OBJECTIVE OF THIS RESEARCH PROGRAM IS TO IMPROVE THE AFFORDABILITY, SURVIVABILITY, AND SERVICE LIFE OF NEXT GENERATION AIRCRAFT THROUGH THE USE OF ADAPT --- AN INTEGRATED ADAPTIVE DIAGNOSTIC AND PROGNOSTIC TOOLBOX. THE SPECIFIC FOCUS OF THE RESEARCH EFFORT IS ADAPTIVE DIAGNOSTIC AND PROGNOSTIC ALGORITHMS FOR SYSTEMS WITH SLOWLY-VARYING DYNAMICS. MODEL-BASED MACHINERY DIAGNOSTIC AND PROGNOSTIC TECHNIQUES DEPEND UPON HIGH-QUALITY MATHEMATICAL MODELS OF THE PLANT. MODELING UNCERTAINTIES AND ERRORS DECREASE SYSTEM SENSITIVITY TO FAULTS AND DECREASE THE ACCURACY OF FAILURE PROGNOSES. HOWEVER, THE BEHAVIOR OF MANY PHYSICAL SYSTEMS CHANGES SLOWLY OVER TIME AS THE SYSTEM AGES. THESE CHANGES MAY BE PERFECTLY NORMAL AND NOT INDICATIVE OF IMPENDING FAILURES; HOWEVER, IF A STATIC MODEL IS USED, MODELING ERRORS MAY INCREASE OVER TIME, WHICH CAN ADVERSELY AFFECT HEALTH MONITORING SYSTEM PERFORMANCE. CLEARLY, ONE METHOD TO ADDRESS THIS PROBLEM IS TO EMPLOY A MODEL THAT ADAPTS TO SYSTEM CHANGES OVER TIME. THE RISK IN USING DATA-DRIVEN MODELS THAT LEARN ONLINE TO SUPPORT MODEL-BASED DIAGNOSTICS IS THAT THE MODELS MAY "ADAPT" TO A SYSTEM FAILURE, THUS RENDERING IT UNDETECTABLE BY THE DIAGNOSTIC ALGORITHMS. AN INHERENT TRADE-OFF EXISTS BETWEEN ACCURATELY TRACKING NORMAL VARIATIONS IN SYSTEM DYNAMICS AND POTENTIALLY OBSCURING SLOW-ONSET FAILURES BY ADAPTING TO FAILURE PRECURSORS THAT WOULD BE EVIDENT USING STATIC MODELS. THE PROPOSED ADAPT WILL FEATURE AN INNOVATIVE NEW PARAMETER ESTIMATION ALGORITHM AND NEW ADAPTIVE OBSERVER / KALMAN FILTER TECHNIQUES DESIGNED SPECIFICALLY FOR HEALTH MONITORING. THE RESEARCH TEAM OF BARRON ASSOCIATES, INC., THE UNIVERSITY OF VIRGINIA, AND LOCKHEED MARTIN AERONAUTICS COMPANY WILL DEMONSTRATE ADAPT USING A HIGH-FIDELITY ELECTRO-HYDROSTATIC ACTUATOR SIMULATION. POTENTIAL NASA COMMERCIAL APPLICATIONS: THE TOOL DEVELOPED IN THIS SBIR ADDRESSES AN AREA OF THEORETICAL AND PRACTICAL IMPORTANCE TO FACILITATE WIDESPREAD APPLICATION MODEL-BASED HEALTH MONITORING SYSTEMS AND MODEL-BASED FAULT TOLERANT ADAPTIVE CONTROL SYSTEMS. NON-NASA ADAPTIVE HEALTH MONITORING APPLICATIONS WILL TARGET SIGNIFICANT ADVANCES FOR NUMEROUS AEROSPACE AND LAND-BASED SYSTEMS, INCLUDING MILITARY FIXED-WING AIRCRAFT, UNMANNED AIR VEHICLES, MILITARY AND CIVILIAN LAND-BASED VEHICLES, SHIPBOARD SYSTEMS, AND COMMERCIAL AND GENERAL AVIATION AIRCRAFT. THE VAST ARRAY OF CORPORATIONS AND FEDERALLY-FUNDED ENTITIES CURRENTLY ENGAGED IN PROGNOSTICS AND HEALTH MANAGEMENT RESEARCH AND DEVELOPMENT CREATES THE POTENTIAL FOR A LARGE CONTRACT R&D MARKET. FURTHERMORE, THE PROPOSED ADAPT TECHNOLOGY PROVIDES A NATURAL COMPLEMENT TO OTHER ADVANCED INTELLIGENT VEHICLE CONTROL PRODUCTS ALREADY UNDER DEVELOPMENT AT BARRON ASSOCIATES.
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