본문 바로가기
KERD
논문KCI 등재

Research on unsupervised condition monitoring method of pump-type machinery in nuclear power plant

Zhu Yihu|Xia Hong|Wang Zhichao|Zhang Jiyu|Fu Yin
한국원자력학회 (Korean Nuclear Society)|2024.06|Nuclear Engineering and Technology|제56권 제6호|pp. 2220-2238|DOI 10.1016/j.net.2024.01.031

2

조회

0

다운로드

0

인용

0

참고문헌

키워드

KPCACondition monitoringNuclear power plantRotating machineryDDAE
주제분류원자력공학

초록

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants

Abstract

As a typical active equipment, pump machinery is widely used in nuclear power plants. Although the mechanism of pump machinery in nuclear power plants is similar to that of conventional pumps, the safety and reliability requirements of nuclear pumps are higher in complex operating environments. Once there is significant performance degradation or failure, it may cause huge security risks and economic losses. There are many pumps mechanical parameters, and it is very important to explore the correlation between multi-dimensional variables and condition. Therefore, a condition monitoring model based on Deep Denoising Autoencoder (DDAE) is constructed in this paper. This model not only ensures low false positive rate, but also realizes early abnormal monitoring and location. In order to alleviate the influence of parameter time-varying effect on the model in long-term monitoring, this paper combined equidistant sampling strategy and DDAE model to enhance the monitoring efficiency. By using the simulation data of reactor coolant pump and the actual centrifugal pump data, the monitoring and positioning capabilities of the proposed scheme under normal and abnormal conditions were verified. This paper has important reference significance for improving the intelligent operation and maintenance efficiency of nuclear power plants

참고문헌 (30)

  1. S. Zhu, "Condition monitoring model for sensors of reactor coolant pump based on PCA", Nucl. Power Eng, 41, pp.170~176 (2020)
  2. Y. Yu, "isolation and reconstruction of sensors in nuclear power plant", Ann. Nucl. Energy, 148, pp.107662~ (2020)
  3. W. Li, "Fault detectability analysis in PCA method during condition monitoring of sensors in a nuclear power plant", Ann. Nucl. Energy, 119, pp.342~351 (2018)
  4. H. Wang, "Research on Health Condition Monitoring Method of Wind Turbine Based on Data-Driven", Doctor, pp.2020
  5. L. Eren, "A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier", Journal of Signal Processing Systems, 91, pp.179~189 (2019)
  6. X. Wu, "Rub-impact fault diagnosis of rotating machinery based on 1-D convolutional neural networks", IEEE Sensor. J, 20, pp.8349~8363 (2019)
  7. X. Guo, "Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis", Measurement, 93, pp.490~502 (2016)
  8. Y. Han, "An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes", Comput. Ind, 107, pp.50~58 (2019)
  9. X. Wu, "A hybrid classification autoencoder for semisupervised fault diagnosis in rotating machinery", Mech. Syst. Signal Process, 149, pp.107327~ (2021)
  10. X. Li, "Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning", Chin. J. Aeronaut, 33, pp.418~426 (2020)
  11. H. Shao, "A novel deep autoencoder feature learning method for rotating machinery fault diagnosis", Mech. Syst. Signal Process, 95, pp.187~204 (2017)
  12. G. Kang, "Deep architecture for high-speed railway insulator surface defect detection : denoising autoencoder with multitask learning", IEEE Trans. Instrum. Meas, 68, pp.2679~2690 (2018)
  13. Y. Yu, "A continuous learning monitoring strategy for multi-condition of nuclear power plant", Ann. Nucl. Energy, 164, pp.108544~ (2021)
  14. P. Fu, "Anomaly detection algorithm based on the local distance of densitybased sampling data", J. Software, 28, pp.2625~2639 (2017)
  15. F. Yuan, "Theories and applications of auto-encoder neural networks : a literature survey", Chin. J. Comput, 42, pp.203~230 (2019)
  16. J. Tong, "Rolling bearing fault diagnosis method based on enhanced deep autoencoder network", China Mech. Eng, 32, pp.2617~2624 (2021)
  17. J. Lai, "Review on autoencoder and its application", J. Commun, 42, pp.218~230 (2021)
  18. J. Chen, "A method for detecting anomaly conditions of wind turbines using stacked denoising autoencoders with sliding window and multiple noise ratios", Trans. China Electrotech. Soc, 35, pp.346~358 (2020)
  19. W. Yu, "Robust monitoring and fault isolation of nonlinear industrial processes using denoising autoencoder and elastic net", IEEE Trans. Control Syst. Technol, 28, pp.1083~1091 (2019)
  20. Y. Xiong, "Robust feature extraction for geochemical anomaly recognition using a stacked convolutional denoising autoencoder", Math. Geosci, 54, pp.623~644 (2022)
  21. N. Renstrom, "System-wide anomaly detection in wind turbines using deep autoencoders", Renew. Energy, 157, pp.647~659 (2020)
  22. Y. Zhang, "An enhanced convolutional neural network for bearing fault diagnosis based on time-frequency image", Measurement, 157, pp.107667~ (2020)
  23. J. Yu, "Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes", J. Process Control, 92, pp.119~136 (2020)
  24. T. Wu, "Research on condition monitoring technology for nuclear power plant equipment based on kernel principal component analysis", Nucl. Power Eng, 41, pp.132~137 (2020)
  25. Q. Jiang, "Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference", IEEE Trans. Ind. Electron, 63, pp.377~386 (2015)
  26. X. Ai, "Research on Intelligent Fault Diagnostic Method for Primary Loop System of Nuclear Power Plant", Master, pp.2020
  27. X. Liang, "A sparse autoencoder-based unsupervised scheme for pump fault detection and isolation", Appl. Sci, 10, pp.6789~ (2020)
  28. Y. Fang, "Multi-objective optimization of sum of squares double exponentially weighted moving average chart and its application in output sensor fault detection", Control Decis, 37, pp.1656~1664 (2022)
  29. S. Zhu, "A robust strategy for sensor fault detection in nuclear power plants based on principal component analysis", Ann. Nucl. Energy, 164, pp.108621~ (2021)
  30. G. Liu, "A decision-making method for machinery abnormalities based on neural network prediction and Bayesian hypothesis testing", Electronics, 10, pp.1610~ (2021)

관련 논문AI추천

Imbalanced sample fault diagnosis method for rotating machinery in nuclear power plants based on deep convolutional conditional generative adversarial networkWang Zhichao, Xia Hong, Yang Bo, Yin Wenzhe, Zhang Jiyu한국원자력학회 · Nuclear Engineering and Technology2023 · 제55권 제6호Cavitation state identification of centrifugal pump based on CEEMD-DRSNChen Zeyu, Dai Cui, Dong Liang, Hu Siyuan, Zhang Yuhang한국원자력학회 · Nuclear Engineering and Technology2023 · 제55권 제4호Discrimination model using denoising autoencoder-based majority vote classification for reducing false alarm rate김시우, 유경탁, 이헌용한국원자력학회 · Nuclear Engineering and Technology2023 · 제55권 제10호Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial network and random forestDu Baigang, Guo Jun, Liu Shiqiao, Sun Xiang, Wang Yulong한국원자력학회 · Nuclear Engineering and Technology2024 · 제56권 제12호Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVMChen Zeyu, Dong Liang, Fan Chuanhan, Hua Runan, Hu Siyuan, Xiao xingxin한국원자력학회 · Nuclear Engineering and Technology2023 · 제55권 제3호Fault diagnosis of nuclear power plant sliding bearing-rotor systems using deep convolutional generative adversarial networksChen Feiyu, Huang Guobing, Li Qi, Wang Xiaojing, Xiong Xin, Yuan Weimin, Zhang Weiwei한국원자력학회 · Nuclear Engineering and Technology2024 · 제56권 제8호

이 간행물의 다른 논문 (49건)

A comparative study of 3D printing and sol-gel polymer production techniques: A case study on usage of ABS polymer for radiation shieldingGultekin Batuhan, Ogul Hasan, Us Hakan, Bulut Fatihpp. 1943-1949PWR core calculation based on pin-cell homogenization in three-dimensional pin-by-pin geometryYu Junchong, Li Qing, Li Yunzhao, Chai Xiaoming, Wu Hongchun, Zhang Bin, Zhao Wenbo, Fang Chao, Gong Zhaohupp. 1950-1958Possibilities of reinforcement learning for nuclear power plants: Evidence on current applications and beyondChen Yangkun, Li Xiu, Zhang Junjie, Gong Aichengpp. 1959-1974Methodology for numerical evaluation of fracture resistance under pinch loading of spent nuclear fuel cladding containing reoriented hydrides김세연, 이상훈pp. 1975-1988Event diagnosis method for a nuclear power plant using meta-learning이희재, 이대일, 김종현pp. 1989-2001
원문 보기