Anomaly detection of control rod drive mechanism using long short-term memory based autoencoder and extreme gradient boosting

Jing Chen; Ze-Shi Liu; Hao Jiang; Xi-Ren Miao; Yong Xu

doi:10.1007/s41365-022-01111-0

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Anomaly detection of control rod drive mechanism using long short-term memory based autoencoder and extreme gradient boosting

NUCLEAR ENERGY SCIENCE AND ENGINEERING | Updated：2022-11-22

- Anomaly detection of control rod drive mechanism using long short-term memory based autoencoder and extreme gradient boosting
- Nuclear Science and Techniques Vol. 33, Issue 10, Article number: 127(2022)
- Affiliations：
  
  1.College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
  2.The Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
  3.Fujian Fuqing Nuclear Power Co., Ltd., Fujian 350318, China
- Author bio：
  
  xuyongxmu@gmail.com
- Funds：
- DOI：10.1007/s41365-022-01111-0
  CLC：
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Jing Chen, Ze-Shi Liu, Hao Jiang, et al. Anomaly detection of control rod drive mechanism using long short-term memory based autoencoder and extreme gradient boosting. [J]. Nuclear Science and Techniques 33(10):127(2022)
DOI：

Jing Chen, Ze-Shi Liu, Hao Jiang, et al. Anomaly detection of control rod drive mechanism using long short-term memory based autoencoder and extreme gradient boosting. [J]. Nuclear Science and Techniques 33(10):127(2022) DOI： 10.1007/s41365-022-01111-0.

摘要

Abstract

Anomaly detection for the control rod drive mechanism (CRDM) is key to enhancing the security of nuclear power plant equipment. In CRDM real-time condition-based maintenance, most existing methods cannot deal with long sequences and periodic abnormal events and have poor feature extraction from these data. In this paper, a learning-based anomaly detection method employing a long short-term memory-based autoencoder (LSTM-AE) network and an extreme gradient boosting (XGBoost) algorithm is proposed for the CRDM. The nonlinear and sequential features of the CRDM coil currents can be automatically and efficiently extracted by the LSTM neural units and AE network. The normal behavior LSTM-AE model was established to reconstruct the errors when feeding abnormal coil current signals. The XGBoost algorithm was leveraged to monitor the residuals and identify outliers for the coil currents. The results demonstrate that the proposed anomaly detection method can effectively detect different timing sequence anomalies and provide a more accurate forecasting performance for CRDM coil current signals.

关键词

Keywords

Anomaly detectionCRDMLSTM-AEResidualsXGBoost

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