{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T05:23:48Z","timestamp":1787376228919,"version":"build-2736575974"},"reference-count":23,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Nuclear Reactor Systems Design for Open Projects","award":["SQ-KFKT-24-2021-006"],"award-info":[{"award-number":["SQ-KFKT-24-2021-006"]}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"crossref","award":["2025JJ70159"],"award-info":[{"award-number":["2025JJ70159"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Research on Safety-Critical Technologies of Instrument and Control System","award":["230SKJ001"],"award-info":[{"award-number":["230SKJ001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Minor leaks in the reactor coolant system (RCS) can provide early warning of degradation that may progress towards safety-relevant failures in nuclear power plants. Their timely localization remains difficult because they generate weak thermal-hydraulic perturbations and labeled leak data are rarely available. Here, we present MATCA-TM, a physics-informed zero-shot framework that combines a Multi-scale Attention Temporal Convolutional Autoencoder (MATCA) with theoretical response-template matching for early leak localization. The framework learns multivariate temporal dependencies solely from normal operating data, enabling reconstruction residuals to highlight subtle anomalies caused by leaks. Physics-informed templates constructed from thermal-hydraulic response characteristics then provide a physically motivated basis for localizing leak sources. On the II + CPR1000 simulation platform, which represents a CPR1000 pressurized water reactor model, MATCA-TM achieved 65.3% localization accuracy for an equivalent leakage diameter of 6.7 \u00d7 10\u22123 cm and outperformed the evaluated machine learning and deep learning baselines under the same protocol. By combining data-driven representation learning with domain physics, MATCA-TM provides a physics-informed, simulation-validated approach to diagnosing incipient RCS leaks and a basis for further validation under plant-relevant operating conditions.<\/jats:p>","DOI":"10.3390\/s26165289","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:05:19Z","timestamp":1787241919000},"page":"5289","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate Classification of Minor Leaks in Primary Loop Systems Using a Multi-Scale Attention Temporal Convolutional Autoencoder"],"prefix":"10.3390","volume":"26","author":[{"given":"Jinghua","family":"Yang","sequence":"first","affiliation":[{"name":"School of Nuclear Science and Technology, University of South China, Hengyang 421001, China"},{"name":"Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang 421001, China"},{"name":"Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Yang","sequence":"additional","affiliation":[{"name":"Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang 421001, China"},{"name":"Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China"},{"name":"School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang 421001, China"},{"name":"Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China"},{"name":"School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoran","family":"Xu","sequence":"additional","affiliation":[{"name":"Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang 421001, China"},{"name":"Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China"},{"name":"School of Computer Science, University of South China, Hengyang 421001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guorui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology, University of South China, Hengyang 421001, China"},{"name":"Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang 421001, China"},{"name":"Key Laboratory of Advanced Nuclear Energy Design and Safety, Ministry of Education, Hengyang 421001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113256","DOI":"10.1016\/j.nucengdes.2024.113256","article-title":"Review and Identification on Thermal Hydraulic Phenomena Related to Core Make-up Tank in PWR","volume":"425","author":"Liu","year":"2024","journal-title":"Nucl. Eng. Des."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105943","DOI":"10.1016\/j.pnucene.2025.105943","article-title":"Research on the Short-Term Heat Release and Pressure Relief System of HPR1000-Based Suppressive Containment System under Typical Accidents","volume":"189","author":"Sun","year":"2025","journal-title":"Prog. Nucl. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2748","DOI":"10.1016\/j.net.2022.01.028","article-title":"Feasibility Study of \u03b2-Ray Detection System for Small Leakage from Reactor Coolant System","volume":"54","author":"Jang","year":"2022","journal-title":"Nucl. Eng. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"105396","DOI":"10.1016\/j.pnucene.2024.105396","article-title":"Development of 16N Monitoring System for Real-Time Detection of Small Leakage in Reactor Coolant Systems","volume":"176","author":"Kim","year":"2024","journal-title":"Prog. Nucl. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1016\/j.net.2023.12.027","article-title":"Relative Humidity Prediction of a Leakage Area for Small RCS Leakage Quantification by Applying the Bi-LSTM Neural Networks","volume":"56","author":"Lee","year":"2024","journal-title":"Nucl. Eng. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102001","DOI":"10.1088\/1361-6501\/ad6203","article-title":"A survey on fault diagnosis of rotating machinery based on machine learning","volume":"35","author":"Wang","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110531","DOI":"10.1016\/j.engappai.2025.110531","article-title":"Loss of Coolant Accident Monitoring and Pipe Break Diagnosis in Pressurized Water Reactors Using Bayesian-Optimized Long Short-Term Memory Models","volume":"149","author":"Obra","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, C., Chen, P., Jiang, F., Xie, J., and Yu, T. (2023). Fault Diagnosis of Nuclear Power Plant Based on Sparrow Search Algorithm Optimized CNN-LSTM Neural Network. Energies, 16.","DOI":"10.3390\/en16062934"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104502","DOI":"10.1016\/j.pnucene.2022.104502","article-title":"Fault Diagnosis Based on Gated Recurrent Unit Network with Attention Mechanism and Transfer Learning under Few Samples in Nuclear Power Plants","volume":"155","author":"Qian","year":"2023","journal-title":"Prog. Nucl. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"124358","DOI":"10.1016\/j.energy.2022.124358","article-title":"Transfer Learning Network for Nuclear Power Plant Fault Diagnosis with Unlabeled Data under Varying Operating Conditions","volume":"254","author":"Li","year":"2022","journal-title":"Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"111690","DOI":"10.1016\/j.nucengdes.2022.111690","article-title":"Transfer Learning with Limited Labeled Data for Fault Diagnosis in Nuclear Power Plants","volume":"390","author":"Li","year":"2022","journal-title":"Nucl. Eng. Des."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"136357","DOI":"10.1016\/j.energy.2025.136357","article-title":"Unsupervised Clustering Research of Nuclear Power Plants under Unlabeled Unknown Fault Diagnosis Scenario","volume":"326","author":"Zhou","year":"2025","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103331","DOI":"10.1016\/j.net.2024.11.033","article-title":"Automatic Classification of Multi-Channel PSD Results Combining Unsupervised and Supervised Learning","volume":"57","author":"Song","year":"2025","journal-title":"Nucl. Eng. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"136204","DOI":"10.1016\/j.energy.2025.136204","article-title":"Nuclear Power Systems Unsupervised Anomaly Localization Considering Spatiotemporal Information and Influence Mechanism between Devices","volume":"325","author":"Wang","year":"2025","journal-title":"Energy"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qi, B., Xiao, X., Liang, J., Zhou, H., Wu, W., and Zhang, X. (2026). Synergistic AI for Nuclear Safety: PINNs, XAI, DTs, and LLMs in Fault Diagnosis. Nucl. Technol., 1\u201318.","DOI":"10.1080\/00295450.2025.2583848"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yang, J., Yang, X., Liu, J., Xu, Z., and Huang, G. (Prog. Nucl. Energy, 2026). Detecting the Minor Leaks in the Primary Loop System Based on a Multi-Scale Attention Temporal Convolutional Autoencoder, Prog. Nucl. Energy, submitted.","DOI":"10.3390\/s26165289"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yuan, B., Zheng, J., Wang, J., Zeng, H., Yang, W., Huang, H., and Zhang, S. (2023). Numerical Calculation Scheme of Neutronics-Thermal-Mechanical Coupling in Solid State Reactor Core Based on Galerkin Finite Element Method. Energies, 16.","DOI":"10.3390\/en16020659"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mazrou, H., Lababsa, D., and Hamed, D. (2026). Advancing Neutronic\u2013Thermal-Hydraulic Coupling for Safety Assessment of MTR-Type Research Reactors: Application to Steady-State and Transient Analysis of OPAL Research Reactor. SSRN, preprint.","DOI":"10.2139\/ssrn.6442180"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112689","DOI":"10.1016\/j.nucengdes.2023.112689","article-title":"The Development of Nuclear Reactor Three-Dimensional Neutronic Thermal\u2013Hydraulic Coupling Code: CorTAF-2.0","volume":"415","author":"Dong","year":"2023","journal-title":"Nucl. Eng. Des."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111665","DOI":"10.1016\/j.anucene.2025.111665","article-title":"High-Fidelity 3D Neutronics and Thermal-Hydraulics Coupling Analysis of a Liquid Metal-Cooled Fast Reactor","volume":"223","author":"Guo","year":"2025","journal-title":"Ann. Nucl. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., and Pei, D. (2019). Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM.","DOI":"10.1145\/3292500.3330672"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., and Ng, S.-K. (2019). MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. Artificial Neural Networks and Machine Learning\u2013ICANN 2019: Text and Time Series, Springer.","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"ref_23","first-page":"1409","article-title":"A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data","volume":"33","author":"Zhang","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5289\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T04:25:55Z","timestamp":1787372755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,20]]},"references-count":23,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165289"],"URL":"https:\/\/doi.org\/10.3390\/s26165289","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,20]]}}}