NASA SBIR 2006 Solicitation

FORM B - PROPOSAL SUMMARY


PROPOSAL NUMBER:06 A1.07-9512
SUBTOPIC TITLE:Integrated Vehicle Health Management
PROPOSAL TITLE:Real-Time Adaptive Algorithms for Flight Control Diagnostics and Prognostics

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Barron Associates, Inc.
1410 Sachem Place, Suite 202
Charlottesville, VA 22901-0807
(434) 973-1215

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Jason O Burkholder
burkholder@bainet.com
1410 Sachem Place, Suite 202
Charlottesville, VA  22901-0807
(434) 973-1215

TECHNICAL ABSTRACT ( Limit 2000 characters, approximately 200 words)
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.

POTENTIAL NASA COMMERCIAL APPLICATIONS ( Limit 1500 characters, approximately 150 words)
The proposed research effort clearly offers the potential for a significant leap in vehicle performance, op-eration, safety, cost, and capability. The technology will require a demonstration in an actual-flight envi-ronment to fully characterize and validate the performance that is predicted in simulation and demon-strated in wind tunnel experiments. The research is particularly relevant to NASA's Intelligent Flight Con-trol System (IFCS), which has the objective of enabling a pilot to land an aircraft that has suffered a major systems failure or combat damage, and also to the Single Aircraft Accident Prevention thrust of the Avia-tion Safety Program in which Barron Associates has participated for a number of years.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS ( Limit 1500 characters, approximately 150 words)
Prognostic and health management systems are becoming increasingly common in aviation, marine, and industrial applications due to the potential operational improvements and cost savings. The generic, open-architecture modeling, diagnostic, and prognostic software developed under this research program will be suitable for many military and commercial applications.

NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.

TECHNOLOGY TAXONOMY MAPPING
Autonomous Reasoning/Artificial Intelligence
Expert Systems


Form Printed on 09-08-06 18:19