Journal of Advanced Marine Engineering and Technology

The Korean Society of Marine Engineering (한국마린엔지니어링학회)
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Fault Detection of Governor Systems Using Discrete Wavelet Transform Analysis

  • Received : 2012.06.26
  • Accepted : 2012.07.10
  • Published : 2012.07.31
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Abstract

This study introduces a condition diagnosis technique for a turbine governor system. The governor system is an important control system to handle turbine speed in a nuclear power plant. The turbine governor system includes turbine valves and stop valves which have their own functions in the system. Because a turbine governor system is operated by high oil pressure, it is very difficult to maintain under stable operating conditions. Turbine valves supply oil pressure to the governor system for proper operation. Using the pressure variation of turbine and governor valves, operating conditions of the turbine governor control system are detected and identified. To achieve automatic detection of valve status, time-based and frequency-based analysis is employed. In this study, a new approach, wavelet decomposition, was used to extract specific features from the pressure signals of the governor and stop valves. The extracted features, which represent the operating conditions of the turbine governor system, include important information to control and diagnose the valves. After extracting the specific features, decision rules were used to classify the valve conditions. The rules were generated by a decision tree algorithm (a typical simple method for data-based rule generation). The results given by the wavelet-based analysis were compared to detection results using time- and frequency-based approaches. Compared with the several related studies, the wavelet transform-based analysis, the proposed in this study has the advantage of easier application without auxiliary features.

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References

  1. S. H. Jang and W. P. Baek, Nuclear Safety, Cheong Moon Gak, pp. 1-37, 1998.
  2. Korea Electric Power Research Institute, Development of Digital Control System of Turbine Governor of Nuclear Power Plant, Technical Report, Korea Electric Power Research Institute, 2003.
  3. Electric Power Research Institute, Governor Control Logic Drawing of Kori Nuclear Power Plant #2, Technical Report, Electric Power Research Institute, 2004.
  4. J. P. Kim, B. M. Goo, and J. G. Park, "Development of water quality monitoring and diagnosis system of the secondary circuit of the nuclear power station," Power Research, vol. 1, pp. 303-315, 1994.
  5. W. R. Nelson, "REACTOR: An expert system for diagnosis and treatment of nuclear reactor accidents," Proceedings Association for the Advancement of Artificaial Inteligence, pp. 296-301, 1982.
  6. W. R. Nelson, "Response Trees and Expert System for nuclear reactor operation," Technical Report NUREG/CR-3631, Idaho National Engineering Laboratory, EG & G Idaho, 1984.
  7. Hyeon Bae, Yountae Kim, Gyeongdong Baek, Byung-Wook Jung, Sungshin Kim, and Jung-Pil Shin, "Diagnosis of turbine valves in the kori nuclear power plant using fuzzy Logic and neural networks," Lecture Notes in Computer Science, vol. 4493, pp. 641-650, 2007.
  8. Korea Electric Power Corporation, Control System of the Turbine Governor, Technical Report, Korea Electric Power Corporation, 1995.
  9. GEC, Sub-Contractor's Instrumentation Book 30 (Electro-Hydraulic Governor), GEC MANUAL, vol. 4, 1982.
  10. R. Agrawal, K. I. Lin, H. S. Sawhney, and K. Shim, "Fast similarity search in the presence of noise, scaling, and translation in times-series databases," Proceedings of the 21st International Conference on Very Large Data Bases, pp. 490-500, 1995.
  11. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, "Fast subsequence matching in time-series databases," Proceedings of the ACM SIGMOD Conference, Mineapolis, MN, pp. 419-429, 1994.
  12. Y. S. Moon, K. Y. Whang, and W. K. Loh, "Efficient time-series subsequence matching using duality in constructing windows," Information Systems, vol. 26, no. 4), pp. 279-293, 2001. https://doi.org/10.1016/S0306-4379(01)00021-7
  13. Dina Q. Goldin and Paris C. Kanellakis, "On similarity queries for timeseries data: Constraint specification and implementation," Proceedings of the 1st International Conference on Principles and Practice of Constraint Programming (CP'95), Cassis, France, 1995.
  14. Davood Rafiei and Alberto O. Mendelzon, "Similarity-based queries for time series data," Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 13-25, USA, 1997.
  15. Davood Rafiei and Alberto O. Mendelzon, "Efficient retrieval of similar time sequences using DFT", Proceedings of the 5th International Conference on Foundation of Data Organization, Japan, 1998.
  16. Kin-Pong Chan and Ada Wai-Chee Fu, "Efficient time series matching by wavelets," Proceedings of the 15th International Conference on Data Engineering, pp. 126-133, 1999.
  17. Yi-Leh Wu, Divyakant Agrawal, and Amr El Abbadi, "A comparison of DFT and DWT based similarity search in time-series databases," Proceedings of the ninth international conference on Information and knowledge management, pp. 488-495, 2000.
  18. Kin-Pong Chan, Ada Wai-Chee Fu, and Clement T. Yu, "Haar wavelets for efficient similarity search of time-series: With and without time warping," IEEE Transactions on Knowledge and Data Engineering, vol 15, no. 3, pp. 686-705, 2003. https://doi.org/10.1109/TKDE.2003.1198399
  19. Ivan Popivanov and Renee J. Miller, "Similarity search over time-series data using wavelets," Proceedings of the 18th International Conference on Data Engineering, pp. 212-221, 2002.
  20. I. Daubechies, Ten Lectures on Wavelets. SIAM, 1992.
  21. C. Goutte, "On clustering fMRI time series," NeuroImage, vol. 9, no. 3, pp. 298-310. 1999. https://doi.org/10.1006/nimg.1998.0391
  22. K. Kalpakis, D. Gada, and V. Puttagunta, "Distance measures for effective clustering of ARIMA time-series," Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM'01), pp. 273-280, 2001.
  23. Eamonn Keogh Michail Vlachos, Jessica Lin and Dimitrios Gunopulos, "A wavelet-based anytime algorithm for k-means clustering of time series," Proceedings of Workshop on Clustering High Dimensionality Data and Its Applications, 2003.
  24. Jarke J. van Wijk and Edward R. van Selow, "Cluster and calendar based visualization of time series data," Proceedings of IEEE Symposium on Information Visualization, pp. 4-9, 1999.
  25. O. Tim, L. Firoiu, and P. Cohen, "Clustering time series with hidden Markov models and dynamic time warping," Proceedings of the IJCAI-99 Workshop on Neural, Symbolic and Reinforcement Learning Methods for Sequence Learning, pp. 17-21, 1999.
  26. T. C. Fu, F. L. Chung, V. Ng, and R. Luk, "Pattern discovery from stock time series using self-organizing maps," Workshop Notes of KDD2001 Workshop on Temporal Data Mining, pp. 27-37, 2001.
  27. Daniel T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, Wiley, 2004.
  28. Wavelet $Toolbox^{TM}$4.2, $MATLAB^{(R)}$, The MathWorks, Inc., 01 Mar 2008.