Enhancing Nuclear Safeguards with Time Series Sketching-Based Nuclear Material Loss Tracking
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Detecting nuclear material loss or leak events is a critical challenge for nuclear safeguards and material accountancy in the recycling of used nuclear fuel. The MAYER (Multi-Sensor Assimilation Yielding Enhanced Reliability) project, part of the ARPA-E CURIE (Converting UNF Radioisotopes Into Energy) program, aims to develop a framework for tracking material loss in nuclear facilities by integrating multisensor data with predictive modeling. In this paper, we introduce MAterial Loss Tracking via Time Series Sketching (MALTS), a deep learning-based method designed to detect material loss events across the nuclear fuel recycling system. MALTS enhances the accuracy and robustness of nuclear material loss tracking by employing time series sketching to capture essential patterns while filtering out sensor noise, resulting in more stable predictions despite sensor noise effects. This approach also improves the time efficiency of material loss tracking by reducing the dimensionality of highfrequency sensor data, thereby enhancing computational scalability and enabling real-time inference. To further provide insights into the leak, MALTS ranks anomalous channels by post-processing results with a pretrained vision-language model (VLM) that considers the system flow diagram, generating a sorted list of anomalous channels from upstream to downstream. The initial leak location is identified as the first upstream channel. Experimental results demonstrate MALTS’s effectiveness and efficiency in accurately identifying unseen nuclear material loss events and pinpointing initial leak locations, making it suitable for deployment within the MAYER digital twin framework for nuclear material safeguards.
How to Cite
##plugins.themes.bootstrap3.article.details##
nuclear material loss monitoring, time series sensor data, anomaly detection
Bai, S., Kolter, J. Z., & Koltun, V. (2018b). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
Beck, M., P'c5Noppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., . . . Hochreiter, S. (2024). xlstm: Extended long short-term memory. arXiv preprint arXiv:2405.04517.
Cho, K., Van Merri'c5Nenboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Curie program by arpa-e. (2022). https://arpa-e.energy.gov/news-and-events/news-and-insights/us-department-energy-awards-38-million-projects-leading-used-nuclear-fuel-recycling-initiative. (Accessed: 2022-10-21)
Dennis, D., Acar, D. A. E., Mandikal, V., Sadasivan, V. S., Saligrama, V., Simhadri, H. V., & Jain, P. (2019). Shallow rnn: accurate time-series classification on resource constrained devices. Advances in neural information processing systems, 32.
Filonov, P., Kitashov, F., & Lavrentyev, A. (2017). Rnn-based early cyber-attack detection for the tennessee eastman process. arXiv preprint arXiv:1709.02232.
Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735'961780. Honnold, P., Shoman, N., & Cipiti, B. (2024). Increased complexity in simulating measurement systems.
Huang, H., Shah, T., Evans, S., & Yoo, S. (2024). Energy efficient streaming time series classification with attentive power iteration. In Proceedings of the aaai conference on artificial intelligence (Vol. 38, pp. 12574'9612582).
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., & Hager, G. D. (2017). Temporal convolutional networks for action segmentation and detection. In proceedings of the ieee conference on computer vision and pattern recognition (pp. 156'96165).
Likhomanenko, T., Xu, Q., Synnaeve, G., Collobert, R., & Rogozhnikov, A. (2021). Cape: Encoding relative positions with continuous augmented positional embeddings.
Advances in Neural Information Processing Systems, 34, 16079'9616092. Luo, D., & Wang, X. (2024). Moderntcn: A modern pure convolution structure for general time series analysis. In The twelfth international conference on learning representations (pp. 1'9643).
Mayer arpa-e. (2023). https://arpa-e.energy.gov/programs-and-initiatives/search-all-projects/monochromatic-assays-yielding-enhanced-reliability-mayer. (Accessed: 03-2023)
McMath, G., Lousteau, A., & Smith, S. (2024). Nuclear material accounting and control measurements. In Nondestructive assay of nuclear materials for safeguards and security (pp. 709'96717). Springer.
Ravanelli, M., Brakel, P., Omologo, M., & Bengio, Y. (2018). Light gated recurrent units for speech recognition. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(2), 92'96102.
Shoman, N., & Honnold, P. (2022). Limitations for data-driven safeguards at enrichment facilities. (Tech. Rep.). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
Shoman, N., & Moosir, P. M. (2023). Open-source software for material accountancy analysis (Tech. Rep.). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
Taylor, S., & Terentiev, V. (1998). Us national nuclear material control and accounting system (Tech. Rep.). Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).
Tu, F.-F., Liu, D.-J., Yan, Z.-W., Jin, X.-B., & Geng, G.-G. (2024). Stft-tcan: A tcn-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems. Computers & Security, 144, 103961.
Tuli, S., Casale, G., & Jennings, N. R. (2022). Tranad: Deep transformer networks for anomaly detection in multivariate time series data. arXiv preprint arXiv:2201.07284.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Xu, J., Wu, H., Wang, J., & Long, M. (2021). Anomaly transformer: Time series anomaly detection with association discrepancy. arXiv preprint arXiv:2110.02642.
Yu, L., Lu, Q., & Xue, Y. (2024). Dtaad: Dual tcnattention networks for anomaly detection in multivariate time series data. Knowledge-Based Systems, 295, 111849.
Zhai, S., Likhomanenko, T., Littwin, E., Busbridge, D., Ramapuram, J., Zhang, Y., . . . Susskind, J. M. (2023). Stabilizing transformer training by preventing attention entropy collapse. In International conference on machine learning (pp. 40770'9640803).
Zhao, M., Peng, H., Li, L., & Ren, Y. (2024). Multivariate time series anomaly detection based on spatial-temporal network and transformer in industrial internet of things. Computers, Materials & Continua, 80(2).}
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.