Prognostics and Health Management (PHM) for Digital Electronics Using Existing Parameters and Measurands

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$1,015,151.00
Award Year:
2007
Program:
SBIR
Phase:
Phase II
Contract:
N68335-07-C-0172
Award Id:
75259
Agency Tracking Number:
N052-093-0040
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
6595 North Oracle Road, Suite 153B, Tucson, AZ, 85704
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
157955597
Principal Investigator:
Justin Judkins
Principal Investigator
(520) 742-3300
justin@ridgetop-group.com
Business Contact:
Douglas Goodman
President and CEO
(520) 742-3300
doug@ridgetop-group.com
Research Institute:
n/a
Abstract
With its SBIR Partners Boeing, University of Tennessee and HRL Laboratories, Ridgetop develops novel techniques to determine system State-of-Health, and predicting failures in Digital Processors before they occur, and develop improved Remaining Useful Life (RUL) predictions. This will employ the use of existing measurands and operands. Current Area Prognostic Health Management (PHM) Managers and Air Vehicle Managers use on-board software reasoners to reduce ambiguities and provide a level of automated diagnostic intelligence. This SBIR will add integrated aggressive fault detection (FD) and fault identification (FI) capabilities for life-limited digital systems using robust techniques for processing sensor and flight data. Ridgetop will insert innovative and powerful new reasoning technologies into the CPU Board platform and logistics framework so as to provide advancements to current electronic prognostic capabilities on military systems. These enhancements will work within the current PHM architecture to support the aims of the JSF Autonomic Logistics System by providing health awareness, remaining useful life (RUL) predictions, and condition-based maintenance (CBM) actions. The approach involves extraction of externally-accessible test points, and processes the data sets using historical trending techniques, and Bayesian Networks to maximize fault coverage and prognostic accuracy, and minimization of false alarms. These techniques will increase component reliability margins and system availability goals while reducing (through improved root cause analysis) costly sources of "No Trouble Found", "Retest OK" or "Cannot Duplicate" problems.

* information listed above is at the time of submission.

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