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Real-Time Adaptive Algorithms for Flight Control Diagnostics and Prognostics

Award Information

Agency:
National Aeronautics and Space Administration
Branch:
N/A
Award ID:
83887
Program Year/Program:
2007 / SBIR
Agency Tracking Number:
066015
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Barron Associates, Inc.
1410 Sachem Place Suite 202 Charlottesville, VA 22901-2496
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2007
Title: Real-Time Adaptive Algorithms for Flight Control Diagnostics and Prognostics
Agency: NASA
Contract: NNL07AA72P
Award Amount: $99,376.00
 

Abstract:

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 fail-ures; however, if a static a priori model is used, modeling errors may increase over time, which can ad-versely effect 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 ren-dering it undetectable by the diagnostic algorithms. An inherent trade-off exists between accurately track-ing normal variations in system dynamics and potentially obscuring slow-onset failures by adapting to failure precursors that would be evident using static models. Barron Associates, Inc. and the University of Virginia propose an innovative solution that brings together Barron Associates' proven model-based diagnostic and prognostic algorithms with adaptive system identi-fication algorithms enhanced specifically for health monitoring applications that would benefit from online learning.

Principal Investigator:

Jason O. Burkholder
Principal Investigator
4349731215
burkholder@bainet.com

Business Contact:

Connie R. Hoover
Business Official
4349731215
hoover@bainet.com
Small Business Information at Submission:

Barron Associates, Inc.
1410 Sachem Place, Suite 202 Charlottesville, VA 22901

EIN/Tax ID: 541243694
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No