Interactive Abnormal Condition Sign Discovery for Hydroelectric Power Plants

  • Norihiko Ito
  • Takashi Onoda
  • Hironobu Yamasaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5433)


Kyushu Electric Power Co.,Inc. collects various sensor data and weather information to maintain hydroelectric power plants while the plants are running. However, it is very rare to occur abnormal and trouble condition data in power equipments. And in order to collect the abnormal and trouble condition data, it is hard to construct an experimental hydroelectric power plant. Because its cost is very high. In this situation, we have to find abnormal condition data as a risk management. In this paper, we consider that the abnormal condition sign may be unusual condition data. This paper shows results of unusual condition data of bearing vibration detected from the collected various sensor data and weather information by using one class support vector machine. The result shows that our approach may be useful for unusual condition data detection and maintaining hydroelectric power plants. Therefore, the proposed method is one of risk management for hydroelectric power plants.


Data Mining Abnormal Condition Detection Support Vector Machine Hydroelectric Power Plant 


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  1. 1.
    Yamana, M., Murata, H., Onoda, T., Oohashi, T., Kato, S.: Comparison of pattern classification methods in system for crossarm reuse judgment on the basis of rust images. In: Proceedings of Artificial Intelligence and Applications 2005, pp. 439–444 (2005)Google Scholar
  2. 2.
    Jardine, A.K.S.: Repairable system reliability: Recent developments in CBM optimization. In: 19th International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management (COMADEM), Luleä, Sweden, June 13-15 (2006)Google Scholar
  3. 3.
    Tsang, A.H.C., Yeung, W.K., Jardine, A.K.S., Leung, P.K.: Data management for CBM optimization. Journal of Quality in Maintenance Engineering 12, 37–51 (2006)CrossRefGoogle Scholar
  4. 4.
    Lin, D., Banjevic, D., Jardine, A.K.S.: Using principal components in a proportional hazards model with applications in condition-based maintenance. The Journal of the Operational Researche Sciety 57, 910–919 (2006)CrossRefGoogle Scholar
  5. 5.
    Zuashkiani, A., Banjevic, D., Jardine, A.K.S.: Incorporating expert knowledge when estimating parameters of the proportional hazards model. In: Proceedings of Reliability and Maintainability Symposium, Newport Beach, CA, January 23-26 (2006)Google Scholar
  6. 6.
    Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.: New support vector algorithms. Neural Computation 12, 1083–1121 (2000)CrossRefGoogle Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)Google Scholar
  8. 8.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  9. 9.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Norihiko Ito
    • 1
  • Takashi Onoda
    • 1
  • Hironobu Yamasaki
    • 2
  1. 1.System Engineering Research LaboratoryCentral Research Institute of Electric Power IndustryTokyoJapan
  2. 2.The Power System Engineering DepartmentKyushu Electric Power Co.,Inc.FukuokaJapan

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