Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Support vector ensemble for incipient fault diagnosis in nuclear plant components

  • Ayodeji, Abiodun (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) ;
  • Liu, Yong-kuo (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University)
  • Received : 2017.10.31
  • Accepted : 2018.07.20
  • Published : 2018.12.25
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Abstract

The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper.

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References

  1. C. Price, N. Taylor, Multiple fault diagnosis from FMEA, in: IAAI-97 Proceedings, 1997. Retrieved from: www.aaai.org.
  2. E.R. McDermott, R.J. Mikulak, M.R. Beauregard, The Basics of FMEA, Productivity, Inc, New York, 1996.
  3. A. Ayodeji, Y.K. Liu, H. Xia, Knowledge base operator support system for nuclear power plant fault diagnosis, Prog. Nucl. Energy 105 (2018) 42-50. https://doi.org/10.1016/j.pnucene.2017.12.013.
  4. M. Alamaniotis, A. Ikonomopoulos, L.H. Tsoukalas, Online surveillance of nuclear power plant peripheral components using support vector regression, in: Proceedings of the International Symposium on Future I&C for Nuclear Power Plants, Cognitive Systems Engineering on Process Control, and International Symposium on Symbiotic Nuclear Power Systems, Daejeon, Korea, 2011, p. 1230.
  5. A. Ayodeji, Y.K. Liu, SVR optimization with soft computing algorithms for incipient SGTR diagnosis, Ann. Nucl. Energy 121 (2018) 89-100. https://doi.org/10.1016/j.anucene.2018.07.011
  6. A. Widodo, B.S. Yang, Support vector machine in machine condition monitoring and fault diagnosis, Mech. Syst. Signal Process. 21 (2007) 2560-2574. https://doi.org/10.1016/j.ymssp.2006.12.007
  7. I. Yelamosa, G. Escudero, M. Graells, L. Puigjaner, Performance Assessment of a Novel Fault Diagnosis System Based on Support Vector Machines Computers and Chemical Engineering Journal Homepage, 2009. www.elsevier.com/locate/compchemeng.
  8. S. Dash, V. Venkatasubramanian, Challenges in the Industrial Applications of Fault Diagnostic Systems Computers and Chemical Engrn, vol. 24, 2000, pp. 785-791. www.elsevier.com/locate/compehemen. https://doi.org/10.1016/S0098-1354(00)00374-4
  9. D. Wanga, J. Zheng, Y. Zhou, J. Li, A scalable support vector machine for distributed classification in ad hoc sensor networks, Neurocomputing 74 (2010) 394-400. https://doi.org/10.1016/j.neucom.2010.03.016
  10. A.P. Forero, A. Cano, G.B. Giannakis, Consensus-based distributed support vector machines, J. Mach. Learn. Res. 11 (2010) 1663-1707.
  11. N. Cristianini, J.S. Taylor, AN Introduction to Support Vector Machine and Other Kernel Based Methods, Cambridge University Press, New York, NY, USA, 2000. ISBN:0-521-78019-5.
  12. N. Cristianini, B. Scholkopf, Support vector machines and kernel methods: the new generation of learning machines, AI Mag. 23 (3) (2002).
  13. M. Claudio, S. Rocco, E. Zio, A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems, Reliab. Eng. Syst. Saf. 92 (2007) 593-600. https://doi.org/10.1016/j.ress.2006.02.003
  14. J. Liu, E. Zio, An Adaptive Online Learning Approach for Support Vector Regression: Online-SVR-FID Mechanical Systems and Signal Processing, vols. 76-77, 2016, pp. 796-809.
  15. S. Ferdowsil, S. Voloshynovskiy1, M. Gabryel, M. Korytkowski, Multi-class Classification: a Coding Based Space Partitioning International Conference on Artificial Intelligence and Soft Computing, ICAISC, Polish Neural Network Society, 2014.
  16. T. Hastie, R. Tibshirani, Classification by pairwise coupling, in: Proceedings of the Conference on Advances in Neural Information Processing Systems 10, Ser. NIPS '97, MIT Press, Cambridge, MA, USA, 1998, pp. 507-513.
  17. S. Escalera, O. Pujol, P. Radeva, Separability of ternary codes for sparse designs of error-correcting output codes, Pattern Recogn. Lett. 30 (3) (2009) 285-297. https://doi.org/10.1016/j.patrec.2008.10.002
  18. M. Pal, Multiclass Approaches for Support Vector Machine Based Land Cover Classification. Lecture Note, National Institute of Technology Kurukshetra, Department of Civil engineering, 2016.
  19. MATLAB 8.5. (R2015a).The MathWorks Inc., Natick, MA. License number 161052.
  20. J. Weston, C. Watkins, Support vector machines for multi-class pattern recognition, in: Proceedings, European Symposium on Artificial Neural Networks. Bruges, Belgium, 21-23 April, 1999, pp. 219-224.
  21. J. Furnkranz, Round robin classification, J. Mach. Learn. Res. 2 (2002) 721-747.
  22. T. Windeatt, R. Ghaderi, Coding and decoding strategies for multiclass learning problems, Inf. Fusion 4 (1) (2003) 11-21. https://doi.org/10.1016/S1566-2535(02)00101-X
  23. R. Isermann, Fault Diagnosis of Technical Processes-applications, Springer Heidelberg, 2006.
  24. A. Gojavonic, An Introduction to Fault Mode and Effect Analysis, 1996. MSc Thesis.
  25. N.A. Ansari, S. Patil, B. Ghosh, et al., Evaluation of Crack Opening Area and Leak Rate in Various PHT Pipings for LBB Analysis of Indian PHWRS, Bhabha Atomic Research Centre Mumbai, 2000.

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