Autonomous Learning for Condition Based Maintenance

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$69,953.00
Award Year:
2003
Program:
STTR
Phase:
Phase I
Contract:
N0017403C0048
Award Id:
64426
Agency Tracking Number:
03-0065T
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
6022 Constitution Avenue NE, Albuquerque, NM, 87110
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
094142122
Principal Investigator:
Ken Blemel
Vice President
(505) 255-8611
ken_blemel@mgtsciences.com
Business Contact:
Marlene Blemel
President
(505) 255-8611
kay_blemel@mgtsciences.com
Research Institution:
UNIV. OF NEW MEXICO
George Luger
Department of Computer Science
Albuquerque, NM, 87131
(505) 277-3112
Nonprofit college or university
Abstract
Our STTR project will develop a data driven prognostic system that uses automated learning algorithms with stochastic artificial intelligence models to provide advanced warning of failure, fault, and other error events. Our work is based on new theory forimplementing learning algorithms within Bayesian stochastic models that have been developed by computer scientists at the University of New Mexico Artificial Intelligence Group. Bayesian learning is a key enabling technology for accurate autonomous realtime situation assessment from operating signatures of operating equipment. Management Sciences has teamed with UNM to develop and demonstrate a library of predictive engines based on self-learning used with advanced pattern recognition techniques toidentify the early signs of malfunctioning in operating machinery and electronic systems. The predictive engines will be commercialized in Phase II. Autonomous assessment through automated learning will provide breakthroughs for situation awarenessneeded for precise dynamic control, accurate condition assessment, self directed maintenance and precision logistics. The ability to predict machine/equipment events has significant commercial potential in aircraft, power, manufacturing, processing,transportation, and other industrial applications where such capability would allow companies to improve reliability and safety, reduce downtime, and lower the direct maintenance cost of physical assets.

* information listed above is at the time of submission.

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