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Nuclear power plant sensor fault detection using singular value decomposition-based method

Mandal, Shyamapada, Sairam, N, Sridhar, S, Swaminathan, P·2017·Sādhanā
DOI 10.1007/s12046-017-0700-2
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Crossref · 10.1007/s12046-017-0700-2

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Research works printed but not linked: 25

  1. Huang S, Tan K K and Lee T H 2012 Fault diagnosis and fault-tolerant control in linear drives using the Kalman filter. IEEE Trans. Ind. Electron. 59: 4285–4292 · DOI
  2. Zarei J and Poshtan J 2011 Sensor fault detection and diagnosis of a process using unknown input observer. Math. Comput. Appl. 16: 31–42
  3. Gertler J J 1997 Fault detection and isolation using parity relations. Contr. Eng. Pract. 5: 653–661 · DOI
  4. Mehranbod N, Soroush M and Panjapornpon C 2005 A method of sensor fault detection and identification. J. Process Control 15: 321–339 · DOI
  5. Wang S and Cui J 2005 Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method. Appl. Energy 82: 197–213 · DOI
  6. Huang Y, Gertler J and McAvoy T J 2000 Sensor and actuator fault isolation by structured partial PCA with nonlinear extensions. J. Process Control 10: 459–469 · DOI
  7. Wang S and Xiao F 2004 AHU sensor fault diagnosis using principal component analysis method. Energy Build. 36: 147–160 · DOI
  8. Upadhyaya B L and Eryurek E 1992 Application of neural network for sensor validation and plant monitoring. Nuclear Technol. 97: 170–176 · DOI
  9. Huang X 2004 Sensor fault diagnosis and reconstruction of engine control system based on autoassociative neural network. Chinese J. Aeronaut. 17: 23–27 · DOI
  10. Palme T, Fast M and Thern M 2011 Gas turbine sensor validation through classification with artificial neural networks. Appl. Energy 88: 3898–3904 · DOI
  11. Du Z, Fan B, Chi J and Jin X 2014 Sensor fault detection and its efficiency analysis in air handling unit using the combined neural networks. Energy Build. 72: 157–166 · DOI
  12. Hussain S, Mokhtar M and Howe J M 2015 Sensor failure detection, identification, and accommodation using fully connected cascade neural network. IEEE Trans. Ind. Electron. 62: 1683–1692 · DOI
  13. Napolitano M R, Windon D A, Casanova J L, Innocenti M and Silvestri G 1988 Kalman filters and neural-network schemes for sensor validation in flight control systems. IEEE Trans. Control Syst. Technol. 6: 596–611 · DOI
  14. Zhou J, Guob A, Cellerc B and Su S 2014 Fault detection and identification spanning multiple processes by integrating PCA with neural network. Appl. Soft Comput. 14: 4–11 · DOI
  15. Zhu Y, Jin X and Du Z 2012 Fault diagnosis for sensors in air handling unit based on neural network pre-processed by wavelet and fractal. Energy Build. 44: 7–16 · DOI
  16. Chen J, Li H, Sheng D and Li W 2015 A hybrid data-driven modeling method on sensor condition monitoring and fault diagnosis for power plants. Electr. Power Energy Syst. 71: 274–284 · DOI
  17. Hou Z, Lian Z, Yao Y and Yuan X 2006 Data mining based sensor fault diagnosis and validation for building air conditioning system. Energy Convers. Manag. 47: 2479–2490 · DOI
  18. Hotelling H 1933 Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24: 417–441 · DOI
  19. Romagnoli J A and Palazoglu A 2006 Introduction to process control, 2nd edn. Boca Raton, FL: CRC Press · DOI
  20. Wang S and Chen Y 2004 Sensor validation and reconstruction for building central chilling systems based on principal component analysis. Energy Convers. Manag. 45: 673–695 · DOI
  21. Harkat M, Mourot G and Ragot J 2006 An improved PCA scheme for sensor FDI: application to an air quality monitoring network. J. Process Control 16: 625–634 · DOI
  22. Jha S K and Yadav R D S 2011 Denoising by singular value decomposition and its application to electronic nose data processing. IEEE Sensor J. 11: 35–44 · DOI
  23. Diana G and Tommasi C 2002 Cross-validation methods in principal component analysis: a comparison. Stat. Methods Appl. 11: 71–82 · DOI
  24. Jolliffe I 2002 Principal component analysis, 2nd edn. Berlin: Springer
  25. Deza M M and Deza E 2009 Encyclopedia of distances. Heidelberg, London, New York: Springer, pp. 94–95 · DOI

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Metadata

Authors
Mandal, S., Sairam, N., Sridhar, S., & Swaminathan, P.
ORCID
Mandal, Shyamapada
Venue
Sādhanā
Publisher
Springer Science and Business Media LLC
Published
2017-07-27
Volume
42
Issue
9
Pages
1473-1480
ISSN
  • 0256-2499
  • 0973-7677
DOI
10.1007/s12046-017-0700-2
Cited by
10
References
0
Language
en
Licence
www.springer.com/tdm (unspecified)
Funders
Board of Research in Nuclear Sciences (2013/36/42-BRNS/2502)

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