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
|