Robust methods for outlier detection and regression for SHM applications.

Dervilis, N., Antoniadou, I., Barthorpe, R.J. orcid.org/0000-0002-6645-8482 et al. (2 more authors) (2016) Robust methods for outlier detection and regression for SHM applications. International Journal of Sustainable Materials and Structural Systems, 2 (1/2). ISSN 2043-8621

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: Copyright © The Authors(s) 2016. Published by Inderscience Publishers Ltd. This is an Open Access Article distributed under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Keywords: structural health monitoring; SHM; environmental and operational influences; leverage points; outliers; novelty detection.
Dates:
  • Published (online): 4 April 2016
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/K003836/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/J016942/1
Depositing User: Symplectic Sheffield
Date Deposited: 17 Oct 2016 14:39
Last Modified: 17 Oct 2016 14:39
Published Version: http://dx.doi.org/10.1504/IJSMSS.2015.078354
Status: Published
Publisher: Inderscience
Refereed: Yes
Identification Number: https://doi.org/10.1504/IJSMSS.2015.078354

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