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

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-07-C-0170
Agency Tracking Number: N052-093-0228
Amount: $1,369,240.00
Phase: Phase II
Program: SBIR
Awards Year: 2007
Solicitation Year: 2005
Solicitation Topic Code: N05-093
Solicitation Number: 2005.2
Small Business Information
200 Canal View Blvd, Rochester, NY, 14623
DUNS: 073955507
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Patrick Kalgren
 Manager, Electronic Syste
 (585) 424-1990
Business Contact
 Mark Redding
Title: President
Phone: (585) 424-1990
Research Institution
Impact Technologies, LLC (Impact), in collaboration with Georgia Tech, and with consultation from Lockheed Martin, propose to further develop and demonstrate an embeddable diagnostic/prognostic solution for mission critical digital electronic systems present on avionic platforms, specifically targeting JSF applications. The fusion of device level physics-of-failure models derived from fault-to-failure progression data, usage measurement and advanced statistical techniques will enable assessment of remaining useful life of electronic systems. The work performed in Phase I focused on understanding failure mechanisms present in semiconductor devices, specifically the manifestation of failure in microprocessors due to accelerated life testing. The knowledge and techniques applied throughout Phase I ideally lends itself to investigation of failures at a system level. Backed with the intimate knowledge of the cause and effect of semiconductor failure and methods for incipient identification, Impact is in a prime position to transition focus and leverage knowledge from one digital component to numerous components present on a digital board to deliver a robust system-level prognostic capability. The Phase II program described herein will integrate collaborative diagnostic and prognostic techniques from engineering disciplines including statistical reliability modeling, damage accumulation models, physics of failure modeling, and sensor-based condition monitoring using automated reasoning algorithms. Critical prognostic features extracted from sensed parameters including, but not limited to component temperatures, power consumption, and built-in tests (BIT) results will be analyzed using advanced fault detection and damage accumulation algorithms. Using model-based assessments in the absence of fault indications, and updating the model-based assessments with sensed information when it becomes available provides health state awareness at any point in time.

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

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