Selection of Sensors that Influence Trouble Condition Sign Discovery based on a One-class Support Kernel Machine for Hydroelectric Power Plants

Hiroshi Murata, Yasushi Shinohara, Takashi Onoda

2013

Abstract

Trouble conditions rarely occur in the equipment of hydroelectric power plants. Therefore, it is important to find indicator signs for trouble conditions. In a previous study, we proposed a trouble condition sign discovery method, which consists of two detection stages. In the first stage, we can discover trouble condition signs, which are different from the usual condition data. In the second stage, we can monitor aging degradation, with plant experts confirm these trouble condition signs in daily operations. Hence, there is a need to detect these trouble condition signs using a small number of sensors. In this paper, we propose a method for narrowing down the sensors used in trouble condition sign discovery. This paper shows the experimental results of trouble condition sign detection for bearing vibration based on the collected data from different sensors using our proposed method and our previously proposed method. The experimental results show that even if the number of sensors is reduced, our proposed method can find trouble condition signs, which are different from the usual condition data. Therefore, the proposed method may be useful for trouble condition sign discovery in hydroelectric power plants.

References

  1. Bach, F. R., Lanckriet, G. R. G., and Jordan, M. I. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. In Proceedings of the twenty-first international conference on Machine learning, ICML 7804.
  2. Jardine, A. K., Lin, D., and Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7):1483 - 1510.
  3. Lanckriet, G. R. G., Cristianini, N., Bartlett, P., Ghaoui, L. E., and Jordan, M. I. (2004). Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, (5):27-72.
  4. Onoda, T., Ito, N., and Hironobu, Y. (2009). Trouble condition sign discovery based on support vector machines for hydroelectric power plants. In Proceedings of the 2009 international joint conference on Neural Networks, IJCNN'09, pages 1201-1208.
  5. Schölkopf, B., Smola, A. J., Williamson, R. C., and Bartlett, P. L. (2000). New support vector algorithms. Neural computation, 12(5):1207-1245.
  6. Yamana, M., Murata, H., Onoda, T., Ohashi, T., and Kato, S. (2005). Development of system for crossarm reuse judgment on the basis of classification of rust images using support vector machine. In Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 7805, pages 402-406, Washington, DC, USA. IEEE Computer Society.
Download


Paper Citation


in Harvard Style

Murata H., Shinohara Y. and Onoda T. (2013). Selection of Sensors that Influence Trouble Condition Sign Discovery based on a One-class Support Kernel Machine for Hydroelectric Power Plants . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 466-473. DOI: 10.5220/0004542704660473


in Bibtex Style

@conference{ncta13,
author={Hiroshi Murata and Yasushi Shinohara and Takashi Onoda},
title={Selection of Sensors that Influence Trouble Condition Sign Discovery based on a One-class Support Kernel Machine for Hydroelectric Power Plants},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={466-473},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004542704660473},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Selection of Sensors that Influence Trouble Condition Sign Discovery based on a One-class Support Kernel Machine for Hydroelectric Power Plants
SN - 978-989-8565-77-8
AU - Murata H.
AU - Shinohara Y.
AU - Onoda T.
PY - 2013
SP - 466
EP - 473
DO - 10.5220/0004542704660473