Tsialiamanis, G., Wagg, D., Dervilis, N. orcid.org/0000-0002-5712-7323 et al. (1 more author) (2022) On an application of generative adversarial networks on remaining lifetime estimation. In: Structural Health Monitoring 2021: Enabling Next Generation SHM for Cyber-Physical Systems: Proceedings of the Thirteenth International Workshop on Structural Health Monitoring (IWSHM). Thirteenth International Workshop on Structural Health Monitoring (IWSHM), 15-17 Mar 2022, Stanford University, CA, USA. Destech Publications, Inc. ISBN 9781605956879
Abstract
A major problem of structural health monitoring (SHM) has been the prognosis of damage and the prediction of the remaining useful life of a structure. Both tasks depend on multiple parameters, many of which are often uncertain. A wide range of models have been developed for the aforementioned tasks, but they have been either deterministic or stochastic with the ability to take into account only a restricted set of past states of the structure. In the current work, a generative model is proposed in order to make predictions about the damage evolution of structures. The model is able to perform in a population-based SHM (PBSHM) framework, to take into account many past states of the damaged structure, to incorporate uncertainties in the modelling process and to generate potential damage evolution outcomes according to data acquired from a structure. The algorithm is tested on a simulated damage evolution example and the results reveal that it is able to provide quite confident predictions about the remaining useful life of structures within a population.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2022 DEStech Publishing Inc. This is an author-produced version of a paper subsequently published in Structural Health Monitoring 2021: Enabling Next Generation SHM for Cyber-Physical Systems: Proceedings of the Thirteenth International Workshop on Structural Health Monitoring (IWSHM). Uploaded with permission from the copyright holder. |
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: | 09 Nov 2022 14:04 |
Last Modified: | 11 Nov 2022 14:25 |
Published Version: | http://dx.doi.org/10.12783/shm2021/36287 |
Status: | Published |
Publisher: | Destech Publications, Inc. |
Refereed: | Yes |
Identification Number: | 10.12783/shm2021/36287 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192127 |