Enhanced Prognostic Model for Digital Electronics

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-06-C-0088
Agency Tracking Number: N052-093-0307
Amount: $80,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2005
Solicitation Year: 2005
Solicitation Topic Code: N05-093
Solicitation Number: 2005.2
Small Business Information
INTELLIGENT AUTOMATION, INC.
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
DUNS: 161911532
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Chi-Man Kwan
 VP of Research & Development
 (301) 294-5238
 ckwan@i-a-i.com
Business Contact
 Mark James
Title: Contract & Proposals Manager
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
N/A
Abstract
The ability to predict failures in aircraft electronic boards, their digital component elements and devices have the potential to reduce the risks of unanticipated failures while significantly reduce support costs. In this proposal, Intelligent Automation, Inc. (IAI) and Computer Aided Life Cycle Engineering (CALCE) Electronic Products and Systems Center (EPSC) at the University of Maryland propose an enhanced life consumption monitoring methodology for digital electronic boards and their components. Our approach involves a novel process to conduct Life Consumption Monitoring (LCM), including failure modes and mechanisms analysis (FMMA), virtual reliability assessment, sensor data pre-processing/feature selection, fault detection/identification/isolation, stress and damage accumulation analysis, and remaining life estimation. Meanwhile, the prediction output will be associated with a confidence distribution and adjusted by Support Vector Machine (SVM) and Confidence Prediction Neural Network (CPNN). Key advantages include better prediction of Remaining Useful Life than conventional methods, better prediction of some key parameters (thermal cycles and vibration loads) into the future so that prognostics information can be improved, incorporation of a simplified model that can provide "what if" predictions, and a data driven approach to improve the confidence of the overall predictions.

* information listed above is at the time of submission.

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