A semi-supervised method for the characterization of degradation of nuclear power plants steam generators

Pinciroli, Luca; Baraldi, Piero; Shokry, Ahmed; Zio, Enrico; Seraoui, Rédouane; Mai, Carole

doi:10.1016/j.pnucene.2020.103580

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…Empirical success was shown in classifying experimental and simulated nuclear power plant (NPP) fault scenarios. Another example of SSML used for nuclear safety is Pinciroli et al [4], where a more ad hoc system of feature extraction based on importance for characterization is used. Sun et al [5] use a system of weak supervision to implement a convolutional neural network (CNN) that applies pseudolabels eventually included in future training.…”

Section: Semi-supervised Applications To Nuclear Engineeringmentioning

confidence: 99%

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

Stomps

Wilson

Dayman

et al. 2023

JNE

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The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

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Section: Semi-supervised Applications To Nuclear Engineeringmentioning

confidence: 99%

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

Stomps

Wilson

Dayman

et al. 2023

JNE

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“…During the real-time operation of a nuclear power plant, the parameters such as temperature and pressure are monitored constantly and recorded. When studies based on machine learning are carried out for fault diagnosis or anomaly detection using nuclear power plant operating data, dimensionality reduction is required to avoid the calculation delay caused by the excessive data volume and the interference of weakly related parameters on the prediction accuracy [1][2][3][4][5]. Feature selection is interpretable because it retains the physical meaning of the original feature, which makes it more advantageous in related research [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Correlation-Based Feature Selection Algorithm for Operating Data of Nuclear Power Plants

He¹,

Yu²,

et al. 2021

Science and Technology of Nuclear Installations

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Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. In this paper, a correlation-based feature selection algorithm is developed to implement feature selection of nuclear power plant operating data. The proposed algorithm is verified by experiments and compared with traditional correlation-based feature selection algorithms. The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data.

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A multi-branch deep neural network model for failure prognostics based on multimodal data

Yang

Baraldi

Zio

2021

Journal of Manufacturing Systems

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A semi-supervised method for the characterization of degradation of nuclear power plants steam generators

Cited by 4 publications

References 39 publications

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

A Correlation-Based Feature Selection Algorithm for Operating Data of Nuclear Power Plants

A multi-branch deep neural network model for failure prognostics based on multimodal data

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