Abstract
Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, justification for continued cable use must shift to a condition-based approach since it is cost prohibitive to completely replace cables that are still capable of performing their design function. The Pacific Northwest National Laboratory (PNNL) Accelerated and Real Time Experimental Nodal Analysis (ARENA) cable motor test bed was used to test the response of a commercial spread spectrum time domain reflectometry (SSTDR) system, a laboratory instrument software-controlled SSTDR, and a vector network analyzer-based frequency domain reflectometry (FDR) system to various cable anomalies. The three instrument systems were able to interrogate cables over a range of frequency bandwidths that can be helpful for human data analysis. Data were subjected to supervised and unsupervised machine learning (ML) analyses to distinguish normal undamaged cable responses from anomalous cable responses. Both supervised and unsupervised ML approaches produced encouraging results with an undamaged/anomalous prediction weighted accuracy ranging from 0.69 to 0.87. Recommendations for further development and field implementation include increased and more balanced sample sets particularly including more training data.
| Original language | English |
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| Title of host publication | Proceedings of 2024 51st Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2024 |
| Publisher | American Society of Mechanical Engineers (ASME) |
| ISBN (Electronic) | 9780791888162 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 51st Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2024 - Denver, United States Duration: Jul 21 2024 → Jul 24 2024 |
Publication series
| Name | Proceedings of 2024 51st Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2024 |
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Conference
| Conference | 2024 51st Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2024 |
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| Country/Territory | United States |
| City | Denver |
| Period | 07/21/24 → 07/24/24 |
Funding
This work (PNNL-SA-195124) was sponsored by the U.S. Department of Energy (DOE), Office of Nuclear Energy, for the Light Water Reactor Sustainability (LWRS) Program Materials Research Pathway. The authors extend their appreciation to LiveWire Innovation who supported this work by lending their SSTDR instrument to the program for data acquisition. This work was performed at the Pacific Northwest National Laboratory (PNNL) and at the Idaho National Laboratory (INL). The Pacific Northwest National Laboratory is operated by Battelle for the DOE under Contract DE-AC05-76RL01830. Idaho National Laboratory is a multi-program laboratory operated by Battelle Energy Alliance, LLC for the DOE under contract no. DE-AC07-05ID14517. Neither the U.S. Government, nor any agency thereof, nor any of their employees makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. The U.S. Government retains, and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. Government purposes. The views and opinions of authors expressed herein do not necessarily state or reflect those of the U.S. Government or any agency thereof.