Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data

Bull, L., Worden, K., Fuentes, R. et al. (3 more authors) (2019) Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data. Journal of Sound and Vibration. ISSN 0022-460X

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2019 Elsevier. This is an author-produced version of a paper subsequently published in Journal of Sound and Vibration. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords: Damage detection; Dimension reduction; Outlier analysis; Unsupervised feature extraction; Vibration monitoring
Dates:
  • Accepted: 29 March 2019
  • Published (online): 4 April 2019
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/R004900/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/R003645/1
Depositing User: Symplectic Sheffield
Date Deposited: 10 Apr 2019 14:58
Last Modified: 10 Apr 2019 14:58
Status: Published online
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.jsv.2019.03.025

Download

Accepted Version


Embargoed until: 4 April 2020

Filename: outlier_ensembles_preprint.pdf

Licence: CC-BY-NC-ND 4.0

Request a copy

Share / Export

Statistics