Research ArticleOpen AccessGoogle Scholar indexed
A Novel Real-Time Fault Diagnostic System for Steam Turbine Generator Set by Using Strata Hierarchical Artificial Neural Network
- 1
- 2
- 3
Energy and Power Engineering·Volume 01 (2009)·Pages 7–16·Published 18 August 2009·DOI10.4236/epe.2009.11002
Copy link · social · email
Abstract
The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.
Keywordsreal-timefault diagnosisstrata hierarchical artificial neural networkfuzzy CMAC
- K. N. Gupta, "Vibration—A tool for machine diagnostics and condition monitoring," Sadhana, Vol. 22, No. 3, pp. 393-410, 1997.
- M. S. Patil, Jose Mathew, and P. K. Rajendra Kumar, "Bearing signature analysis as a medium for fault detection: A review," Journal of Tribology, Vol. 130, pp. 1-7, 2008.
- Martin Todd, Stephen D. J. McArthur, J. R. McDonald, and Sally J. Shaw, "A semiautomatic approach to deriving turbine generator diagnostic knowledge," IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol. 37, No. 5, pp. 979-992, 2007.
- Jean-Marc David and Jean-Paul Krivine, "DIVA: Recognition of typical situations for turbine generator diagnosis," Journal of Intelligent and Robotic Systems, Vol. 1, pp. 287-298, 1988.
- Yong-Hwan Bae, Seok-Hee Lee, Ho-Chan Kim, Byung-Ryong Lee, Jaejin Jang, and Jay Lee, "A real-time intelligent multiple fault diagnostic system," International Journal of Advanced Manufacturing Technology, Vol. 29, pp. 590-597, 2006.
- H. R. DePold and F. D. Gass, "The application of expert systems and neural networks to gas turbine prognostics and diagnostics," Journal of Engineering for Gas Turbines and Power, Vol. 121, pp. 607-612, 1999.
- A. El-Shafei, T. A. F. Hassan, A. K. Soliman, Y. Zeyada, and N. Rieger, "Neural network and fuzzy logic diagnostics of 1x faults in rotating machinery," Journal of Engineering for Gas Turbines and Power, Vol. 129, pp. 703-710, 2007.
- Ruqiang Yan and Robert X. Gao, "Rotary machine health diagnosis based on empirical mode decomposition," Journal of Vibration and Acoustics, Vol. 130, pp. 1-12, 2008.
- J. J. Jeng and C. Y. Wei, "An on-line condition monitoring and diagnosis system for feed rolls in the plate mill," Journal of Manufacturing Science and Engineering, Transactions of the ASME, Vol. 124, pp. 52-57, 2002.
- Z. S. Chen, Y. M. Yang, Z. Hu, and G. J. Shen, "Detecting and predicting early faults of complex rotating machinery based on cyclostationary time series model," Journal of Vibration and Acoustics, Transactions of the ASME, Vol. 128, pp. 666-671, 2006.
- Xue Wang and Shuzi Yang, "A parallel distributed knowledge-based system for turbine generator fault diagnosis," Artificial Intelligence in Engineering, Vol. 10, pp. 335-341, 1996.
- Benjamin S. Blanchard and Wolter J. Fabrycky, "Systems engineering and analysis," Prentice-Hall Inc., 1998.