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Autonomous Learning for Condition Based Maintenance

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: N0017403C0048
Agency Tracking Number: 03-0065T
Amount: $69,953.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2003
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
6022 Constitution Avenue NE
Albuquerque, NM 87110
United States
DUNS: 094142122
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ken Blemel
 Vice President
 (505) 255-8611
 ken_blemel@mgtsciences.com
Business Contact
 Marlene Blemel
Title: President
Phone: (505) 255-8611
Email: kay_blemel@mgtsciences.com
Research Institution
 UNIV. OF NEW MEXICO
 George Luger
 
Department of Computer Science
Albuquerque, NM 87131
United States

 (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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