0

Full Content is available to subscribers

Subscribe/Learn More  >

Using a Feasibility Study of Human Computation for Failure Scenario Identification

[+] Author Affiliations
Ryan Arlitt, Chris Hoyle, Robert Stone

Oregon State University, Corvallis, OR

Nikolaos Papakonstantinou

Aalto University, Espoo, Finland

Bryan O’Halloran

Raytheon Missile Systems, Tucson, AZ

Paper No. DETC2014-35363, pp. V01BT02A004; 11 pages
doi:10.1115/DETC2014-35363
From:
  • ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1B: 34th Computers and Information in Engineering Conference
  • Buffalo, New York, USA, August 17–20, 2014
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4629-2
  • Copyright © 2014 by ASME

abstract

Expert opinion is a common resource for identifying potential failure scenarios, but experts can miss novel failure combinations that have no historical precedent. A variety of computational techniques offer their own strengths for failure scenario identification, but can be limited by historical data availability or prescribed models. This paper examines the question of whether a distributed group of non-expert humans can outperform a brute force algorithm in a failure scenario prediction task. This approach uses human intuition to guide solution space exploration, and provides feedback through a system simulation. The results of the paper show that human non-experts outperformed a Monte Carlo simulation, but converge to relatively few critical failure scenarios. These results indicate that while non-expert reasoning may not be directly applicable to effective exploration of many possible failure modes, human computation has the potential to augment or compete with stochastic algorithms in a complex systems failure analysis context.

Copyright © 2014 by ASME
Topics: Computation , Failure

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In