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Fault Diagnosis of Steam Turbine-Generator Sets Using CMAC Neural Network Approach and Portable Diagnosis Apparatus Implementation

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Emerging Intelligent Computing Technology and Applications (ICIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5754))

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Abstract

Based on the vibration spectrum analysis, this paper proposed a CMAC (Cerebellar Model Articulation Controller) neural network diagnosis technique to diagnose the fault type of turbine-generator sets. This novel fault diagnosis methodology contains an input layer, quantization layer, binary coding layer, excited memory addresses coding unit, and an output layer to indicate the fault type possibility. Firstly, we constructed the configuration of diagnosis scheme depending on the vibration fault patterns. Secondly, the known fault patterns were used to train the neural network. Finally, combined with a Visual C++ program the trained neural network can be used to diagnose the possible fault types of turbine-generator sets. Moreover, a PIC microcontroller based portable diagnosis apparatus is developed to implement the diagnosis scheme. All the diagnosis results demonstrate the following merits are obtained at least: 1) High learning and diagnosis speed. 2) High noise rejection ability. 3) Eliminate the weights interference between different fault type patterns. 4) Memory size is reduced by new excited addresses coding technique. 5) Implement easily by chip design technology.

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References

  1. Li, H., Sun, C.X., Hu, X.S., Yue, G., Wang, K.: The Fuzzy Inputting and Outputting Method in Vibration Fault Diagnosis of Steam Turbine-generator Set. Journal of Chongqing University (China) 22(6), 36–40 (1999)

    Google Scholar 

  2. Zhang, G., Yasuoka, K., Ishii, S.: Application of Fuzzy Equivalent Matrix for Fault Diagnosis of Oil-immersed Insulation. In: Proceeding of the 13th International Conference on Dielectric Liquids (ICDL 1999), pp. 400–403 (1999)

    Google Scholar 

  3. Su, Q., Mi, C., Lai, L.L., Austin, P.: A Fuzzy Dissolved Gas Analysis Method for the Diagnosis of Multiple Incipient Faults in a Transformer. IEEE Trans. on Power Systems 15(2), 593–598 (2000)

    Article  Google Scholar 

  4. Dukarm, J.J.: Transformer Oil Diagnosis Using Fuzzy Logic and Neural Networks. In: Canadian Conference on Electrical and Computer Engineering, pp. 329–332 (1993)

    Google Scholar 

  5. Yang, H.T., Liao, C.C., Chou, J.H.: Fuzzy Learning Vector Quantization Networks for Power Transformer Condition Assessment. IEEE Trans. Dielectr. Insulation 8(1) (2001)

    Google Scholar 

  6. Wang, Z., Liu, Y., Griffin, P.J.: A Combined ANN and Expert System Tool for Transformer Fault Diagnosis. IEEE Trans. on PWRD 13(4), 1224–1229 (1998)

    Google Scholar 

  7. Lin, C.E., Ling, J.M., Huang, C.L.: An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis. IEEE Trans. on PWRD 8(1), 231–238 (1993)

    Google Scholar 

  8. Zhang, Y., Ding, X., Liu, Y., Griffin, P.J.: An Artificial Neural Network Approach to Transformer Fault Diagnosis. IEEE Trans. on PWRD 11(4), 1836–1841 (1996)

    Google Scholar 

  9. Yanming, T., Zheng, Q.: DGA Based Insulation Diagnosis of Power Transformer via ANN. In: Proceeding of the sixth Conference on Properties and Applications of Dielectric Materials, pp. 133–136 (1999)

    Google Scholar 

  10. Thang, K.F., Aggarwal, R.K., Esp, D.G., McGrail, A.J.: Statical and Neural Network Analysis of Dissolved in Power Transformers. In: IEE Dielectric Materials, Measurements and Applications Conference Publication, 473 (2000)

    Google Scholar 

  11. Yang, H.T., Chang, W.T., Huang, C.L.: A New Neural Networks Approach to on-line Fault Section Estimation Using Information of Protective Relays and Circuit Breakers. IEEE Trans. on PWRD 9(1), 220–229 (1994)

    Google Scholar 

  12. Wang, M.H., Chao, K.H., Hung, F.C.: The Fault Diagnosis of Power Station Using Neural Network. In: Proceeding of 86 Conference of Technology School on Designated Topic, Taiwan, pp. 102–111 (1997)

    Google Scholar 

  13. Mitchell, J.S.: Machinery Analysis and Monitoring. Pennwell Publishing Company (1981)

    Google Scholar 

  14. Umemara, S.: A New Health Monitoring System for Rotating Machinery. Mitsubishi Juke Giho 18(6), 71–78 (1981)

    Google Scholar 

  15. Li, H., Sun, C.X., Liao, R.J., Chen, W.G., Hu, X.S.: Improved BP Algorithm in Vibration Failure Diagnosis of Steam Turbine-generator Set. Journal of Chongqing University (China) 22(5), 47–52 (1999)

    Google Scholar 

  16. Li, H., Sun, C.X., Hu, X.S., Yue, G., Tang, N.F., Wang, K.: Study of Method on Adaptive Wavelets for Vibration Fault Diagnosis of Steam Turbine-generator Set. Journal of Electrical Engineering (China) 15(3), 57–60 (2000)

    MATH  Google Scholar 

  17. Szu, H., Brian, T.: Neural network adaptive wavelets for signal representation and classification. Optical Engineering 31(9), 1907–1916 (1992)

    Article  Google Scholar 

  18. Brian, T., Szu, H.: Adaptive Wavelets Classification of Acoustic Backscatter and Imagery. Optical Engineering 30(7), 2192–2202 (1994)

    Google Scholar 

  19. Hung, C.P., Wang, M.H., Cheng, C.H., Lin, W.L.: Fault Diagnosis of Steam Turbinegenerator Using CMAC Neural Network Approach. In: Proceeding of International Joint Conference on Neural Network Publication, vol. 334 (2003)

    Google Scholar 

  20. Albus, J.S.: A New Approach to Manipulator Control: the Cerebeller Model Articulation Controller (CMAC)1. Trans. ASME J. Dynam., Syst. Meas. and Contr. 97, 220–227 (1975)

    MATH  Google Scholar 

  21. Hung, C.P., Wang, M.H.: Fault Diagnosis of Air-conditioning System Using CMAC Neural Network Approach. In: Advances in Soft Computing—Engineering, Design and Manufacturing, pp. 1–11. Springer, Heidelberg (2003)

    Google Scholar 

  22. Hung, C.P., Wang, M.H.: Diagnosis of Incipient Faults in Power Transfers Using CMAC Neural Network Approach. Electric Power Systems Research 71, 235–244 (2004)

    Article  Google Scholar 

  23. Zhu, J.N., Miao, S.E., Wang, C.Q.: Implementation of Long-distance Vibration Monitor and Fault Diagnosis System for High Output Turbo-generator Units. Turbine Technology (China) 45(1), 5–7 (2003)

    Google Scholar 

  24. Chen, C.: The Study and Application of Turbo-generator Vibration Fault Diagnosis Based on Multi-symptom Input Neural Network Method. Master dissertation of Zhejiang University (2006)

    Google Scholar 

  25. Handeiman, D.A., Lane, S.H., Gelfand, J.J.: Integrating Neural Networks and Knowledge-based Systems for Intelligent Robotic Control. IEEE Control Syst. Mag., 77–86 (1990)

    Google Scholar 

  26. Wong, Y.F., Sideris, A.: Learning Convergence in the Cerebellar Model Articulation Controller. IEEE Trans. on Neural Network 3(1), 115–121 (1992)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, National Chin-Yi University of Technology, 35, 215 Lane, Sec.1, Chung-Shan Road, Taiping, Taichung, Taiwan

    Chin-Pao Hung, Wei-Ging Liu & Hong-Zhe Su

Authors
  1. Chin-Pao Hung
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  2. Wei-Ging Liu
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  3. Hong-Zhe Su
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, Nam-Ku, 680 - 749, Ulsan, Korea

    Kang-Hyun Jo

  3. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Hong-Hee Lee

  4. School of Electrical Engineering, University of Ulsan, South Korea

    Hee-Jun Kang

  5. e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Str. Prov. per Casamassima Km., 3-70010, Valenzano, (BA), Italy

    Vitoantonio Bevilacqua

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© 2009 Springer-Verlag Berlin Heidelberg

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Hung, CP., Liu, WG., Su, HZ. (2009). Fault Diagnosis of Steam Turbine-Generator Sets Using CMAC Neural Network Approach and Portable Diagnosis Apparatus Implementation. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_78

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  • DOI: https://doi.org/10.1007/978-3-642-04070-2_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04069-6

  • Online ISBN: 978-3-642-04070-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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