Networked Automated Remote Monitoring System

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA9101-10-M-0004
Agency Tracking Number: F093-217-1073
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2009
Solicitation Topic Code: AF093-217
Solicitation Number: 2009.3
Small Business Information
International Electronic Machines
850 River St., Troy, NY, 12180
DUNS: 188282131
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: Y
Principal Investigator
 Ronald Gamache
 Senior Scientist
 (518) 268-1636
Business Contact
 Robert Foss
Title: Manager of Research & Development
Phone: (518) 268-1636
Research Institution
By one survey estimate, more than 100 million Wireless Sensor Networks (WSN) will be deployed over the next five years, many in Condition Based maintenance (CBM) applications. It is estimated that the labor cost for changing batteries between 2006 and 2012 will be $1.1 billion. As a result significant initiatives are attempting to address this issue on several fronts; power harvesting, ultra low power sensors, ultra low power wireless communication hardware and network architectures, and long life high power density batteries. While progress has been made, several of the critical enabling technologies, such as practical power harvesting, are largely un-realized. The pace of WSN deployment has resulted in another challenge; how to turn the volumes of data into useful diagnostic or, better yet, prognostic information. IEM will leverage significant related development in ultra low power or self powered WSNs complemented by a sensor fusion back end to produce a cost effective, essentially hands off, Networked Automated Remote Monitoring System (NARMS). NARMS will attack the problem at the system level, providing a modular, adaptable integrated system with innovations in ultra low power sensing nodes, integrated power harvesting, and novel low power wireless hardware and network protocols. BENEFIT: Reduced capital expenditures and reduced operations and maintenance budgets have driven interest in Condition-Based Maintenance systems. The capability developed in the proposed work will address two of the major shortcomings of current machinery monitoring technology; maintenance cost and disposal issues with battery powered systems, and providing useful prognostic data to enhance maintenance decision making. The developed self powered prognostic health monitoring capability will have wide applicability in power generation systems, industrial manufacturing facilities, infrastructure (bridges, buildings), aircraft, automotive, agriculture, and security.

* information listed above is at the time of submission.

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