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research-article

A Robust Approach to Quantification of Margin and Uncertainty

[+] Author and Article Information
Daniel J. Segalman

Department of Mechanical Engineering Michigan State University East Lansing, MI 48824
segalman@egr.MSU.edu

Thomas Paez

Thomas Paez Consulting, 12605 Osito Ct NE Albuquerque, NM 87111
tlpaez4444@gmail.com

Lara Bauman

Livermore, CA
lara.bauman@gmail.com

1Corresponding author.

ASME doi:10.1115/1.4036180 History: Received October 07, 2015; Revised February 26, 2017

Abstract

A systematic approach to defining margin in a manner that incorporates statistical information and accommodates data uncertainty, but does not require assumptions about specific forms of the tails of distributions is developed. A margin that is insensitive to the character of the tails of the relevant distributions (Tail Insensitive Margin, TIM) is defined. This is complemented by the calculation of probability of failure were the load distribution augmented by a quantity equal to the TIM. This approach avoids some of the perplexing results common to traditional reliability theory where, on the basis of very small amounts of data, one is led to extraordinary claims of infinitesimal probability of failure. Additionally, this approach permits a more meaningful separation of statistical and engineering issues.

Sandia National Laboratories (SNL)
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