Gardner, P. and Barthorpe, R.J. orcid.org/0000-0002-6645-8482 (2019) On current trends in forward model-driven SHM. In: Chang, F.-K. and Kopsaftopoulos, F., (eds.) Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT). 12th International Workshop on Structural Health Monitoring (IWSHM), 10-12 Sep 2019, Stanford, CA, USA. DEStech Publications , pp. 2152-2160. ISBN 9781605956015
Abstract
Forward model-driven approaches to Structural Health Monitoring (SHM) are a category of methods in which validated physics-based models are used to generate data for machine learning classifiers. These approaches were developed to address the lack of available damage state data; which is often a problem in many SHM contexts due to it being impractical or infeasible to collect. Many data-driven approaches to SHM are successful when the appropriate damage state data are available, however the problem of obtaining data for various damage states of interest restricts their use in industry. With this aim, several forward model-driven techniques have been developed in recent years focusing on issues in generating validated physical-models that produce damage state predictions without the need for a damage state data. This paper presents the current state of forward model-driven techniques within the literature. In addition, several key technology areas are highlighted with a demonstration of the benefits and challenges a forward model-driven framework provides.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2019 DEStech Publications. |
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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Mar 2020 12:28 |
Last Modified: | 16 Mar 2020 12:28 |
Published Version: | http://www.dpi-proceedings.com/index.php/shm2019/a... |
Status: | Published |
Publisher: | DEStech Publications |
Refereed: | Yes |
Identification Number: | 10.12783/shm2019/32351 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158396 |