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Stochastic Modeling of Damage Physics for Mechanical Component Prognostics Using Condition Indicators

[+] Author Affiliations
David He, Ruoyu Li

University of Illinois-Chicago, Chicago, IL

Eric Bechhoefer

Goodrich Sensor and Integrated Systems, Vergennes, VT

Paper No. DETC2009-86483, pp. 1259-1264; 6 pages
doi:10.1115/DETC2009-86483
From:
  • ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2: 29th Computers and Information in Engineering Conference, Parts A and B
  • San Diego, California, USA, August 30–September 2, 2009
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4899-9 | eISBN: 978-0-7918-3856-3
  • Copyright © 2009 by ASME

abstract

The health of a mechanical component deteriorates over time and its service life is randomly distributed and can be modeled by a stochastic deterioration process. For most of the mechanical components, the deterioration process follows a certain physical laws and their mean life to failure can be determined approximately by these laws. However, it is not easy to apply these laws for mechanical component prognostics in current health monitoring applications. In this paper, a stochastic modeling methodology for mechanical component prognostics with condition indicators used in current health monitoring applications is presented. The effectiveness of the methodology is demonstrated with a real shaft fatigue life prediction case study.

Copyright © 2009 by ASME
Topics: Physics , Modeling

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