Hembert Pierre, Ghnatios Chady, Cotton Julien, Chinesta Francisco
PIMM Laboratory, Arts et Métiers Institute of Technology, Centre National de la Recherche Scientifique (CNRS), 151 Boulevard de l'Hôpital, 75013 Paris, France.
Andra, French National Radioactive Waste Management Agency, 92298 Châtenay-Malabry, France.
Sensors (Basel). 2024 Feb 29;24(5):1580. doi: 10.3390/s24051580.
A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor's integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network's local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra's Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra's URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.
一个用于放射性废物的深层地质处置库，比如法国国家放射性废物管理机构（Andra）的地质处置研究中心（Cigéo）项目，需要进行长期（持续）监测。为实现这一目标，需采集来自传感器网络的数据。由于环境影响（放射性、电池的机械老化等），该网络会随着时间推移而退化，评估每个传感器的完整性并确保数据一致性对于精确监测这些设施至关重要。图神经网络（GNN）适用于检测复杂网络中的故障传感器，因为它们能准确描述系统中发生的物理现象，并在预测中考虑传感器网络的局部结构。在这项工作中，我们利用在Andra地下研究实验室（URL）获取的实验数据来训练一个图神经网络，用于评估数据完整性。这项工作中考虑的实验模拟了高放废物（HLW）示范单元的热负荷（即核废料对包容单元的加热）。使用在Andra深层地质层的URL中获取的真实实验数据是这项工作的创新点之一。所使用的模型是一个GNN，它输入来自传感器（当前和过去步骤）的温度场，并返回每个单独传感器的状态，即是否有故障。这项工作的另一个创新点在于应用了GraphSAGE模型，该模型结合图网络框架的元素进行了修改，以检测故障传感器，网络中一次最多有一半的传感器出现故障。这种故障传感器的比例是由分布式传感器（光纤）的使用以及对单元的环境影响所导致的。最终，将基于实验数据训练的GNN与其他标准分类方法（阈值法、人工神经网络等）进行了比较，结果证明了它们在评估数据完整性方面的有效性。
