Bull, L.A., Gardner, P.A. orcid.org/0000-0002-1882-9728, Gosliga, J. et al. (7 more authors) (2021) Foundations of population-based SHM, Part I : homogeneous populations and forms. Mechanical Systems and Signal Processing, 148. 107141. ISSN 0888-3270
Abstract
In Structural Health Monitoring (SHM), measured data that correspond to an extensive set of operational and damage conditions (for a given structure) are rarely available. One potential solution considers that information might be transferred, in some sense, between similar systems. A population-based approach to SHM looks to both model and transfer this missing information, by considering data collected from groups of similar structures. Specifically, in this work, a framework is proposed to model a population of nominally-identical systems, such that (complete) datasets are only available from a subset of members. The SHM strategy defines a general model, referred to as the population form, which is used to monitor a homogeneous group of systems. First, the framework is demonstrated through applications to a simulated population, with one experimental (test-rig) member; the form is then adapted and applied to signals recorded from an operational wind farm.
Metadata
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | ||||
Keywords: | Population-based structural health monitoring; Pattern recognition; Wind turbine monitoring | ||||
Dates: |
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Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) | ||||
Funding Information: |
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 17 Aug 2020 11:00 | ||||
Last Modified: | 17 Aug 2020 11:00 | ||||
Status: | Published | ||||
Publisher: | Elsevier | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1016/j.ymssp.2020.107141 |