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Autonomous Sensor Health Monitoring for Modern Shipboard Control Systems

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N65538-06-M-0032
Agency Tracking Number: N052-132-0388
Amount: $69,933.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N05-132
Solicitation Number: 2005.2
Timeline
Solicitation Year: 2005
Award Year: 2005
Award Start Date (Proposal Award Date): 2005-11-07
Award End Date (Contract End Date): 2006-05-06
Small Business Information
1410 Sachem Place, Suite 202
Charlottesville, VA 22901
United States
DUNS: 120839477
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jason Burkholder
 Sr. Research Scientist
 (434) 973-1215
 burkholder@bainet.com
Business Contact
 David Ward
Title: President
Phone: (434) 973-1215
Email: barron@bainet.com
Research Institution
N/A
Abstract

Next generation U.S. Navy shipboard control systems are being designed to provide much higher levels of machinery automation than their predecessors. The U.S. Navy is investing in these sophisticated systems with expectations of improved system performance and reliability. Moreover, reductions in the number of shipboard personnel required to operate the engineering plant may result in lower total ownership costs for these modern ships. Sensors distributed throughout the ship, numbering in the thousands, provide the required inputs to the automated ship control system. The cost in labor and materials to maintain and calibrate these sensors presents a risk to the anticipated cost savings. Furthermore, untimely sensor failures could cause the crew to lose confidence in the machinery control system. Barron Associates, Inc. (BAI) and its partner, Sperry Marine, a Unit of Northrop Grumman Systems Corporation, propose to develop an autonomous sensor health monitoring system that will leverage BAI's proven diagnostic techniques and Sperry's shipboard control system application expertise and development facilities. The development of a comprehensive fault and failure anomaly detection and isolation system will bring together flight-tested algorithms developed by BAI for online, real-time parameter identification and generic algorithms developed by BAI for sensor monitoring in any complex dynamical system.

* Information listed above is at the time of submission. *

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