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http://dx.doi.org/10.1016/j.net.2018.08.020

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)
Publication Information
Nuclear Engineering and Technology / v.51, no.1, 2019 , pp. 146-154 More about this Journal
Abstract
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.
Keywords
PCA; Data pre-processing; False alarm reducing; Sensor FDI;
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1 H.M. Hashemian, On line monitoring applications in nuclear power plants, Prog. Nucl. Energy 53 (2011) 167-181.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
9 H.-B. Jun, D. Kim, A Bayesian network-based approach for fault analysis, Expert Syst. Appl. 81 (2017) 332-348.   DOI
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.   DOI
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.   DOI
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.   DOI
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.   DOI
16 A. Kusiak, Z. Song, Sensor fault detection in power plants, J. Energy Eng. 135 (2009) 127-137.   DOI
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.   DOI
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.   DOI
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.   DOI