| 1. |
- Calivá, Francesco, et al.
(författare)
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A deep learning approach to anomaly detection in nuclear reactors
- 2018
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Ingår i: Proceedings of the International Joint Conference on Neural Networks. ; 2018-July
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Konferensbidrag (refereegranskat)abstract
- In this work, a novel deep learning approach to unfold nuclear power reactor signals is proposed. It includes a combination of convolutional neural networks (CNN), denoising autoencoders (DAE) and k-means clustering of representations. Monitoring nuclear reactors while running at nominal conditions is critical. Based on analysis of the core reactor neutron flux, it is possible to derive useful information for building fault/anomaly detection systems. By leveraging signal and image pre-processing techniques, the high and low energy spectra of the signals were appropriated into a compatible format for CNN training. Firstly, a CNN was employed to unfold the signal into either twelve or forty-eight perturbation location sources, followed by a k-means clustering and k-Nearest Neighbour coarse-to-fine procedure, which significantly increases the unfolding resolution. Secondly, a DAE was utilised to denoise and reconstruct power reactor signals at varying levels of noise and/or corruption. The reconstructed signals were evaluated w.r.t. their original counter parts, by way of normalised cross correlation and unfolding metrics. The results illustrate that the origin of perturbations can be localised with high accuracy, despite limited training data and obscured/noisy signals, across various levels of granularity.
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| 2. |
- De Sousa Ribeiro, Fabio, et al.
(författare)
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Towards a deep unified framework for nuclear reactor perturbation analysis
- 2018
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Ingår i: 2018 IEEE Symposium Series on Computational Intelligence (IEEE SSCI). - 9781538692769 ; , s. 120-127
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Konferensbidrag (refereegranskat)abstract
- In this paper, we take the first steps towards a novel unified framework for the analysis of perturbations in both the Time and Frequency domains. The identification of type and source of such perturbations is fundamental for monitoring reactor cores and guarantee safety while running at nominal conditions. A 3D Convolutional Neural Network (3DCNN) was employed to analyse perturbations happening in the frequency domain, such as an absorber of variable strength or propagating perturbation. Recurrent neural networks (RNN), specifically Long Short-Term Memory (LSTM) networks were used to study signal sequences related to perturbations induced in the time domain, including the vibrations of fuel assemblies and the fluctuations of thermal-hydraulic parameters at the inlet of the reactor coolant loops. 512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type. If the perturbation is in the frequency domain, a separate fully-connected layer utilises said representations to regress the coordinates of its source. The results showed that the perturbation type can be recognised with high accuracy in all cases, and frequency domain scenario sources can be localised with high precision.
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| 3. |
- Demaziere, Christophe, 1973, et al.
(författare)
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Neutron noise-based anomaly classification and localization using machine learning
- 2020
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Ingår i: International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020. - : EDP Sciences. ; 2020-March, s. 2913-2921
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Konferensbidrag (refereegranskat)abstract
- A methodology is proposed in this paper allowing the classification of anomalies and subsequently their possible localization in nuclear reactor cores during operation. The method relies on the monitoring of the neutron noise recorded by in-core neutron detectors located at very few discrete locations throughout the core. In order to unfold from the detectors readings the necessary information, a 3-dimensional Convolutional Neural Network is used, with the training and validation of the network based on simulated data. In the reported work, the approach was also tested on simulated data. The simulations were carried out in the frequency domain using the CORE SIM+ diffusion-based two-group core simulator. The different scenarios correspond to the following cases: a generic “absorber of variable strength”, axially travelling perturbations at the velocity of the coolant flow (due to e.g. fluctuations of the coolant temperature at the inlet of the core), fuel assembly vibrations, control rod vibrations, and core barrel vibrations. In all those cases, various frequencies were considered and, when relevant, different locations of the perturbations and different vibration modes were taken into account. The machine learning approach was able to correctly identify the different scenarios with a maximum error of 0.11%. Moreover, the error in localizing anomalies had a mean squared error of 0.3072 in mesh size, corresponding to less than 4 cm. The proposed methodology was also demonstrated to be insensitive to parasitic noise and will be tested on actual plant data in the near future.
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| 4. |
- Demaziere, Christophe, 1973, et al.
(författare)
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Noise-based core monitoring and diagnostics – Overview of the CORTEX project
- 2017
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Ingår i: Proc. 3rd DAE-BRNS Symposium on Advances in Reactor Physics (ARP-2017), Mumbai, India, December 6-9, 2017.
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Konferensbidrag (refereegranskat)abstract
- This paper gives an overview of the CORTEX project, which is a Research and Innovation Action funded by the European Union in the Euratom 2016-2017 work program, under the Horizon 2020 framework. CORTEX, which stands for CORe monitoring Techniques and EXperimental validation and demonstration, aims at developing an innovative core monitoring technique that allows detecting anomalies in nuclear reactors, such as excessive vibrations of core internals, flow blockage, coolant inlet perturbations, etc. The technique is based on primarily using the inherent fluctuations in neutron flux recorded by in-core and ex-core instrumentation (often referred to as neutron noise), from which the anomalies will be differentiated depending on their type, location and characteristics. In addition to be non-intrusive and not requiring any external perturbation of the system, the method allows the detection of operational problems at a very early stage. Proper actions could thus be taken by utilities before such problems have any adverse effect on plant safety and reliability.
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| 5. |
- Demaziere, Christophe, 1973, et al.
(författare)
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Overview of the CORTEX project
- 2018
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Ingår i: International Conference on Physics of Reactors, PHYSOR 2018: Reactor Physics Paving the Way Towards More Efficient Systems. ; Part F168384-5, s. 2971-2980
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Konferensbidrag (refereegranskat)abstract
- This paper gives an overview of the CORTEX project, which is a Research and Innovation Action funded by the European Union in the Euratom 2016-2017 work program, under the Horizon 2020 framework. CORTEX, which stands for CORe monitoring Techniques and Experimental validation and demonstration, aims at developing an innovative core monitoring technique that allows detecting anomalies in nuclear reactors, such as excessive vibrations of core internals, flow blockage, coolant inlet perturbations, etc. The technique is based on primarily using the inherent fluctuations in neutron flux recorded by in-core and ex-core instrumentation (often referred to as neutron noise), from which the anomalies will be differentiated depending on their type, location and characteristics. In addition to be non-intrusive and not requiring any external perturbation of the system, the method allows the detection of operational problems at a very early stage. Proper actions could thus be taken by utilities before such problems have any adverse effect on plant safety and reliability. In order to develop a method that can reach a high Technology Readiness Level, the consortium, made of 20 partners, was strategically structured around the required core expertise from all the necessary actors of the nuclear industry, both within Europe and outside. The broad expertise of the consortium members ensures the successful development of new in-situ monitoring techniques.
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| 6. |
- Kollias, Stefanos, et al.
(författare)
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Machine learning for analysis of real nuclear plant data in the frequency domain
- 2022
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Ingår i: Annals of Nuclear Energy. - : Elsevier BV. - 0306-4549 .- 1873-2100. ; 177
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Tidskriftsartikel (refereegranskat)abstract
- Machine Learning is used in this paper for noise-diagnostics to detect defined anomalies in nuclear plant reactor cores solely from neutron detector measurements. The proposed approach leverages advanced diffusion-based core simulation tools to generate large amounts of simulated data with different types of driving perturbations originating at all theoretically possible locations in the core. Specifically the CORE SIM+ modelling framework is employed, which generates these data in the frequency domain. We train using these vast quantities of simulated data state-of-the-art machine and deep learning models which are used to successfully perform semantic segmentation, classification and localisation of multiple simultaneously occurring in-core perturbations. Actual plant data are then considered, provided by two different reactors, including no labels about perturbation existence. A domain adaptation methodology is subsequently developed to extend the simulated setting to real plant measurements, which uses self-supervised, or unsupervised learning, to align the simulated data with the actual plant data and detect perturbations, whilst classifying their type and estimating their location. Experimental studies illustrate the successful performance of the developed approach and extensions are described that indicate a great potential for further research.
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