单传感器AI实时绘制核电站139个点的地震风险

September 9, 2026 Single-sensor AI maps quake risks across 139 points inside a nuclear power plant in real time by National Research Council of Science and Technology edited by Lisa Lock, reviewed by Robert Egan Lisa Lock Scientific Editor Meet our editorial team Behind our editorial process Robert Egan Senior Editor Meet our editorial team Behind our editorial process Editors' notes This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: fact-checked trusted source proofread The GIST Add as preferred source Conceptual diagram of the deep learning model jointly developed by KRISS and UNIST for prioritizing post-earthquake inspections at nuclear facilities. Credit: KRISS and UNIST The Korea Research Institute of Standards and Science (KRISS) has developed a deep learning technology that predicts, in real time, vibrations at multiple locations inside a nuclear power plant using the signal from just a single seismometer and quantifies the risk at each location to enable the prioritization of inspections. In particular, because the AI model can be flexibly designed to match the vibration characteristics of a structure, it is expected to be widely applicable to major industrial facilities such as semiconductor plants and data centers in the future. This research was conducted jointly by KRISS Senior Research Scientist Dr. Lee Jaebeom and the research team of Professor Lee Young-Joo of the Ulsan National Institute of Science and Technology (UNIST), with Lee Jingoo, a student researcher, as the first author of the paper published in Reliability Engineering & System Safety. After the magnitude 5.8 Gyeongju earthquake in 2016, Wolsong Nuclear Power Plant Units 1–4 underwent 80 days of performance testing and detailed inspections before being restarted. Likewise, during the powerful earthquake that struck Kumamoto, Japan, this past July, TSMC's Kumamoto Fab 1 temporarily halted operations and then returned to normal in stages after equipment inspection and adjustment. As these cases show, even when there is no major damage after an earthquake, the process of verifying safety can prolong shutdowns, leading to considerable downtime and economic losses. The problem is that immediately after an earthquake, there is not enough data to determine where to check first. Installing sensors at every major equipment location would allow vibrations at each point to be identified quickly, but this is practically impossible in a nuclear power plant because of constraints such as cable routing, licensing and maintenance within radiation zones. As an alternative, computational analysis can be used to calculate vibrations at locations without sensors, but this takes anywhere from several hours to several days, limiting the ability to respond quickly. To solve this, the research team developed a virtual sensing technology in which AI analyzes the seismic wave signal measured by a single seismometer to infer, in real time, the structural response at 139 points inside a nuclear power plant that have no sensors. Without installing sensors at every location, it can quickly narrow down the areas most affected by an earthquake, and it demonstrated excellent predictive performance even on records from real earthquakes that were not used in training. Furthermore, by incorporating uncertainties arising from factors such as structural characteristics, the technology quantifies, on a scale of 0% to 100%, the probability that the structural response at each location will exceed a predefined risk threshold. Going beyond a binary classification of simply "safe" or "at risk," it ranks locations by risk, helping experts quickly determine inspection priorities. The team also greatly improved the efficiency and versatility of the AI design. The researchers developed a design equation that derives the optimal AI architecture based on a structure's natural frequency, reducing the trial and error of repeatedly designing and comparing multiple models. The AI model designed this way achieved accuracy comparable to that of a state-of-the-art deep learning model with more than 200 times as many parameters, while substantially lowering the computational burden and improving its usability even in resource-limited environments. This has opened up the possibility of extending the technology to a wide range of industrial facilities, including not only nuclear power plants but also semiconductor fabs, data centers and industrial plants. Dr. Jaebeom said, "The key to this research is that it quantifies even the uncertainty in the seismic response prediction results, allowing experts to determine which equipment to inspect first. In the field of safety, AI must not only make accurate predictions but also recognize the limits of its own judgment. Building on this, we will develop trustworthy AI technology that flags uncertain situations to support additional human judgment." More information Jingoo Lee et al, Predicting seismic floor response for nuclear power plant structures with time-series uncertainty propagation using attention-enhanced multimodal deep learning, Reliability Engineering & System Safety (2026). DOI: 10.1016/j.ress.2026.112582 Key concepts AI-enabled digital twins Provided by National Research Council of Science and Technology Who's behind this story? Lisa Lock BA art history, MA material culture. Former museum editor, paramedic, and transplant coordinator. Editing for Science X since 2021. Full profile → Robert Egan Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile → Citation: Single-sensor AI maps quake risks across 139 points inside a nuclear power plant in real time (2026, September 9) retrieved 9 September 2026 from https://techxplore.com/news/2026-09-sensor-ai-quake-nuclear-power.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only. 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