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Comparison of Prognostic Health Management Algorithms for Assessment of Electronic Interconnect Reliability

[+] Author Affiliations
Pradeep Lall

Auburn University, Auburn, AL

Ryan Lowe

Air Force Research Laboratory, Eglin, FL

Kai Goebel

NASA Ames Research Center, Moffett Field, CA

Paper No. IPACK2013-73252, pp. V001T04A019; 10 pages
doi:10.1115/IPACK2013-73252
From:
  • ASME 2013 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems
  • Volume 1: Advanced Packaging; Emerging Technologies; Modeling and Simulation; Multi-Physics Based Reliability; MEMS and NEMS; Materials and Processes
  • Burlingame, California, USA, July 16–18, 2013
  • Conference Sponsors: Electronic and Photonic Packaging Division
  • ISBN: 978-0-7918-5575-1
  • Copyright © 2013 by ASME

abstract

This paper compares three prognostic algorithms applied to the same data recorded during the failure of a solder joint in ball grid array component attached to a printed circuit board. The objective is to expand on the relative strengths and weaknesses of each proposed algorithm. Emphasis will be placed on highlighting differences in underlying assumptions required for each algorithm, details of remaining useful life calculations, and methods of uncertainty quantification. Metrics tailored specifically for Prognostic Health Monitoring (PHM) are presented to characterize the performance of predictions. The relative merits of PHM algorithms based on a Kalman filter, extended Kalman filter, and a particle filter all demonstrated on the same data set will be discussed. The paper concludes by discussing which algorithm performs best given the information available about the system being monitored.

Copyright © 2013 by ASME

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