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A comparison of ultrasonic temperature monitoring using machine learning and physics-based methods for high-cycle thermal fatigue monitoring
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clarkson-et-al-2023-a-comparison-of-ultrasonic-temperature-monitoring-using-machine-learning-and-physics-based-methods.pdf | Published version | 4.11 MB | Adobe PDF | View/Open |
Title: | A comparison of ultrasonic temperature monitoring using machine learning and physics-based methods for high-cycle thermal fatigue monitoring |
Authors: | Clarkson, L Zhang, Y Cegla, F |
Item Type: | Journal Article |
Abstract: | Failure of pipe network components in so-called mixing zones due to high-cycle thermal fatigue (HCTF) can occur within nuclear power plants where fluids of different thermal and hydraulic properties interact. Given that the consequences of such failures are potentially deadly, a method to monitor HCTF non-invasively in real-time is expected to be of great use. This method may be realised by a technique to determine the inaccessible temperature distribution of a component since thermal gradients drive HCTF. Previous work showed that a physics-based method called the inverse thermal modelling (ITM) method can obtain the temperature distribution from external temperature and ultrasonic time of flight (TOF) measurements. This study investigated whether the long-short-term memory (LSTM) machine learning architecture could be a faster alternative to the ITM method for data inversion. On experimental data, a 25-member ensemble of LSTM networks achieved an ensemble median root mean square error (RMSE) of 1.04°C and an ensemble median mean error of 0.194°C (both relative to a resistance temperature device measurement). These values are similar to the ITM method which achieved a RMSE of 1.04°C and a mean error of 0.196°C. The single LSTM network and the ITM method achieved a computation-to-real-world time ratio of 0.008% and 14%, respectively demonstrating that both methods can invert data in real-time. Simulation studies revealed that LSTM performance is sensitive to small differences between the training and real-world parameters leading to unacceptable errors. However, these errors can be detected via an ensemble of independent networks and, corrected by simply adding a correction factor to the TOF prior to being input into the networks. The results show that LSTM has the potential to be an alternative to the ITM method; however, the authors favour ITM for temperature distribution monitoring given its interpretability. |
Issue Date: | May-2024 |
Date of Acceptance: | 1-Aug-2023 |
URI: | http://hdl.handle.net/10044/1/111749 |
DOI: | 10.1177/14759217231190041 |
ISSN: | 1475-9217 |
Publisher: | SAGE Publications |
Start Page: | 1560 |
End Page: | 1577 |
Journal / Book Title: | Structural Health Monitoring: an international journal |
Volume: | 23 |
Issue: | 3 |
Copyright Statement: | © The Author(s) 2023. Creative Commons License (CC BY 4.0) This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
Publication Status: | Published |
Online Publication Date: | 2023-08-07 |
Appears in Collections: | Mechanical Engineering |
This item is licensed under a Creative Commons License