Nuclear Engineering and Technology

  • 제51권1호
  • /
  • Pages.146-154
  • /
  • 2019
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
권호 표지
DOI QR코드

DOI QR Code

Improved PCA method for sensor fault detection and isolation in a nuclear power plant

  • Li, Wei (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Peng, Minjun (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Wang, Qingzhong (China Mobile Group Heilongjiang Company Limited)
  • 투고 : 2017.10.30
  • 심사 : 2018.08.24
  • 발행 : 2019.02.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

키워드

참고문헌

  1. H.M. Hashemian, On line monitoring applications in nuclear power plants, Prog. Nucl. Energy 53 (2011) 167-181. https://doi.org/10.1016/j.pnucene.2010.08.003
  2. D. Richard, K. Frederic, R. Jose, L. Francois, J.-L. Germain, Detection, Isolation and identification of sensor faults in nuclear power plants, IEEE Trans. Contr. Syst. Technol. 5 (1997) 42-60. https://doi.org/10.1109/87.553664
  3. J. Farhan, A. Muhanmmad, H. Inamul, Q.K. Khan, I. Masood, Fault diagnosis of Pakistan Research Reactor-2 with data-driven techniques, Ann. Nucl. Energy 90 (2016) 433-440. https://doi.org/10.1016/j.anucene.2015.12.023
  4. B. Piero, C. Antonio, M. Francesca, Z. Enrico, An ensemble approach to sensor fault detection and signal reconstruction for nuclear system control, Ann. Nucl. Energy 37 (2010) 778-790. https://doi.org/10.1016/j.anucene.2010.03.002
  5. S.W. Wang, J.T. Cui, Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method, Appl. Energy 82 (2005) 197-213. https://doi.org/10.1016/j.apenergy.2004.11.002
  6. Y.P. Hu, G.N. Li, H.X. Chen, H.R. Li, J.Y. Liu, Sensitivity analysis for PCA-based chiller sensor fault detection, Int. J. Refrig. 63 (2016) 133-143. https://doi.org/10.1016/j.ijrefrig.2015.11.006
  7. F. Li, Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems, The University of Tennessee, 2011. PhD thesis.
  8. J.H. Chen, H.K. Li, D.R. Sheng, W. Li, A hybrid data-driven modeling method on sensor condition monitoring and fault diagnosis for power plants, Electr. Power Energy Syst. 71 (2015) 274-284. https://doi.org/10.1016/j.ijepes.2015.03.012
  9. H.-B. Jun, D. Kim, A Bayesian network-based approach for fault analysis, Expert Syst. Appl. 81 (2017) 332-348. https://doi.org/10.1016/j.eswa.2017.03.056
  10. A. Messai, A. Mellit, I. Abdellani, P.A. Massi, On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs, Prog. Nucl. Energy 79 (2015) 8-21. https://doi.org/10.1016/j.pnucene.2014.10.013
  11. X. Xiao, J.W. Hines, E.U. Robert, Sensor validation and fault detection using neural networks, in: Proceedings of Maintenance and Reliability Conference (MARCON), University of Tennessee, 1999.
  12. R. Perla, S. Mukhopadhyay, A.N. Samanta, Sensor fault detection and isolation using neural networks, in: Proceedings of TENCO 2004 IEEE Rdgion10 Conference, D, 2004, pp. 676-679.
  13. K. Salahshoor, M. Kordenstani, M.S. Khoshoro, Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers, Energy 35 (2010) 5472-5482. https://doi.org/10.1016/j.energy.2010.06.001
  14. K.Y. Chen, L.S. Chen, M.C. Chen, C.L. Lee, Using SVM based method for equipment fault detection in a thermal power plant, Comput. Ind. 62 (2011) 42-50. https://doi.org/10.1016/j.compind.2010.05.013
  15. J.P. Ma, J. Jiang, Applications of fault detection and diagnosis methods in nuclear power plants: a review, Prog. Nucl. Energy 53 (2011) 255-266. https://doi.org/10.1016/j.pnucene.2010.12.001
  16. A. Kusiak, Z. Song, Sensor fault detection in power plants, J. Energy Eng. 135 (2009) 127-137. https://doi.org/10.1061/(ASCE)0733-9402(2009)135:4(127)
  17. J.W. Hines, R. Seibert, Technical review of on-line monitoring techniques for performance assessment: state-of-the-Art, Nuclear Regulatory Commission 1 (2006). NUREG/CR-6895.
  18. S. Valle, W.H. Li, S.J. Qin, Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods, Ind. Eng. Chem. Res. 38 (1999) 4389-4401. https://doi.org/10.1021/ie990110i
  19. X. Chen, Research on Data Preprocess Method for Thermal Parameters, North China Electric Power University, 2013. Master thesis.
  20. R.E. Walpole, Probability and Statistics for Engineers and Scientists, ninth ed., Prentice Hall, 2012.
  21. M.Z. Sun, Vibration signal smoothing method based on MATLAB, Electronic Measurement Technology 30 (2007) 55-57.
  22. D. Tomassi, D. Milone, J.D.B. Nelson, Wavelet shrinkage using adaptive structured sparsity constraints, Signal Process. 106 (2015) 73-85. https://doi.org/10.1016/j.sigpro.2014.07.001
  23. J.Z. Liu, X.P. Liu, L. Tian, Combustion control optimization systems based on information fusion technology, East China Electric Power 37 (2009) 2088-2092.
  24. Y.X. Pei, M. Guo, The fundamental principle and application of sliding average method, Gun Launch & Control Journal 1 (2001) 21-24.
  25. T. Chen, On reducing false alarms in multivariate statistical process control, Chem. Eng. Res. Des. 88 (2010) 430-436. https://doi.org/10.1016/j.cherd.2009.09.003

피인용 문헌

  1. Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing vol.2019, 2019, https://doi.org/10.1155/2019/5736104
  2. Dynamic weight-based learning method for data detection in manufacturing vol.12, pp.11, 2019, https://doi.org/10.1177/1687814020971899
  3. Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm vol.2021, 2019, https://doi.org/10.1155/2021/4981394
  4. Quantify Coal Macrolithotypes of a Whole Coal Seam: A Method Combing Multiple Geophysical Logging and Principal Component Analysis vol.14, pp.1, 2019, https://doi.org/10.3390/en14010213
  5. Pattern Recognition-Based Technique for Control Rod Position Identification in Pressurized Water Reactors vol.207, pp.4, 2021, https://doi.org/10.1080/00295450.2020.1792742
  6. Data pre-processing methods for NPP equipment diagnostics algorithms: an overview vol.7, pp.2, 2021, https://doi.org/10.3897/nucet.7.63675