Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic Process
To ensure the safety of nuclear power production, nuclear power plants deploy numerous sensors to monitor various physical indicators during production, enabling the early detection of anomalies. Efficient anomaly detection relies on complete sensor data. However, compared to conventional energy sou...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4358 |
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| author | Linyu Liu Shiqiao Liu Shuan He Kui Xu Yang Lan Huajian Fang |
| author_facet | Linyu Liu Shiqiao Liu Shuan He Kui Xu Yang Lan Huajian Fang |
| author_sort | Linyu Liu |
| collection | DOAJ |
| description | To ensure the safety of nuclear power production, nuclear power plants deploy numerous sensors to monitor various physical indicators during production, enabling the early detection of anomalies. Efficient anomaly detection relies on complete sensor data. However, compared to conventional energy sources, the extreme physical environment of nuclear power plants is more likely to negatively impact the normal operation of sensors, compromising the integrity of the collected data. To address this issue, we propose an anomaly detection method for nuclear power data: Neural Normal Stochastic Process (NNSP). This method does not require imputing missing sensor data. Instead, it directly reads incomplete monitoring data through a sequentialization structure and encodes it as continuous latent representations in a neural network. This approach avoids additional “processing” of the raw data. Moreover, the continuity of these representations allows the decoder to specify supervisory signals at time points where data is missing or at future time points, thereby training the model to learn latent anomaly patterns in incomplete nuclear power monitoring data. Experimental results demonstrate that our model outperforms five mainstream baseline methods—ARMA, Isolation Forest, LSTM-AD, VAE, and NeutraL AD—in anomaly detection tasks on incomplete time series. On the Power Generation System (PGS) dataset with a 15% missing rate, our model achieves an F1 score of 83.72%, surpassing all baseline methods and maintaining strong performance across multiple industrial subsystems. |
| format | Article |
| id | doaj-art-2cc1526f554147bc8e8f890994270fdc |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2cc1526f554147bc8e8f890994270fdc2025-08-20T03:32:27ZengMDPI AGSensors1424-82202025-07-012514435810.3390/s25144358Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic ProcessLinyu Liu0Shiqiao Liu1Shuan He2Kui Xu3Yang Lan4Huajian Fang5China Nuclear Power Operation Technology Corporation, Wuhan 430233, ChinaChina Nuclear Power Operation Technology Corporation, Wuhan 430233, ChinaChina Nuclear Power Operation Technology Corporation, Wuhan 430233, ChinaChina Nuclear Power Operation Technology Corporation, Wuhan 430233, ChinaChina Nuclear Power Operation Technology Corporation, Wuhan 430233, ChinaChina Nuclear Power Operation Technology Corporation, Wuhan 430233, ChinaTo ensure the safety of nuclear power production, nuclear power plants deploy numerous sensors to monitor various physical indicators during production, enabling the early detection of anomalies. Efficient anomaly detection relies on complete sensor data. However, compared to conventional energy sources, the extreme physical environment of nuclear power plants is more likely to negatively impact the normal operation of sensors, compromising the integrity of the collected data. To address this issue, we propose an anomaly detection method for nuclear power data: Neural Normal Stochastic Process (NNSP). This method does not require imputing missing sensor data. Instead, it directly reads incomplete monitoring data through a sequentialization structure and encodes it as continuous latent representations in a neural network. This approach avoids additional “processing” of the raw data. Moreover, the continuity of these representations allows the decoder to specify supervisory signals at time points where data is missing or at future time points, thereby training the model to learn latent anomaly patterns in incomplete nuclear power monitoring data. Experimental results demonstrate that our model outperforms five mainstream baseline methods—ARMA, Isolation Forest, LSTM-AD, VAE, and NeutraL AD—in anomaly detection tasks on incomplete time series. On the Power Generation System (PGS) dataset with a 15% missing rate, our model achieves an F1 score of 83.72%, surpassing all baseline methods and maintaining strong performance across multiple industrial subsystems.https://www.mdpi.com/1424-8220/25/14/4358nuclear power productionanomaly detectionincomplete time seriesneural normal stochastic process |
| spellingShingle | Linyu Liu Shiqiao Liu Shuan He Kui Xu Yang Lan Huajian Fang Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic Process Sensors nuclear power production anomaly detection incomplete time series neural normal stochastic process |
| title | Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic Process |
| title_full | Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic Process |
| title_fullStr | Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic Process |
| title_full_unstemmed | Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic Process |
| title_short | Anomaly Detection in Nuclear Power Production Based on Neural Normal Stochastic Process |
| title_sort | anomaly detection in nuclear power production based on neural normal stochastic process |
| topic | nuclear power production anomaly detection incomplete time series neural normal stochastic process |
| url | https://www.mdpi.com/1424-8220/25/14/4358 |
| work_keys_str_mv | AT linyuliu anomalydetectioninnuclearpowerproductionbasedonneuralnormalstochasticprocess AT shiqiaoliu anomalydetectioninnuclearpowerproductionbasedonneuralnormalstochasticprocess AT shuanhe anomalydetectioninnuclearpowerproductionbasedonneuralnormalstochasticprocess AT kuixu anomalydetectioninnuclearpowerproductionbasedonneuralnormalstochasticprocess AT yanglan anomalydetectioninnuclearpowerproductionbasedonneuralnormalstochasticprocess AT huajianfang anomalydetectioninnuclearpowerproductionbasedonneuralnormalstochasticprocess |