DC MOTOR FAULT DIAGNOSIS BY MEANS OF ARTIFICIAL NEURAL NETWORKS

Krzysztof Patan, Józef Korbicz, Gracjan Głowacki

Abstract

The paper deals with a model-based fault diagnosis for a DC motor realized using artificial neural networks. The considered process was modelled by using a neural network composed of dynamic neuron models. Decision making about possible faults was performed using statistical analysis of a residual. A neural network was applied to density shaping of a residual, and after that, assuming a significance level, a threshold was calculated. Moreover, to isolate faults a neural classifier was developed. The proposed approach was tested in DC motor laboratory system at the nominal operating conditions as well as in the case of faults.

References

  1. Bell, A. J. and Sejnowski, T. J. (1995). An informationmaximization approach to blind separation and blind deconvolution. Neural computation, 7:1129-1159.
  2. Frank, P. M. and K öppen-Seliger, B. (1997). New developments using AI in fault diagnosis. Artificial Intelligence, 10(1):3-14.
  3. Fuessel, D. and Isermann, R. (2000). Hierarchical motor diagnosis utilising structural knowledge and a selflearning neuro-fuzzy scheme. IEEE Trans. Industrial Electronics, 47:1070-1077.
  4. Haykin, S. (1999). Neural Networks. A comprehensive foundation, 2nd Edition. Prentice-Hall, New Jersey.
  5. Korbicz, J., Koscielny, J. M., Kowalczuk, Z., and Cholewa, W., editors (2004). Fault Diagnosis. Models, Artificial Intelligence, Applications. Springer-Verlag, Berlin.
  6. Moseler, O. and Isermann, R. (2000). Application of modelbased fault detection to a brushless dc motor. IEEE Trans. Industrial Electronics, 47:1015-1020.
  7. Patan, K. (2007). Approximation ability of a class of locally recurrent globally feedforward neural networks. In Proc. European Control Conference, ECC 2007, Kos, Greece. accepted.
  8. Patan, K. and Parisini, T. (2005). Identification of neural dynamic models for fault detection and isolation: the case of a real sugar evaporation process. Journal of Process Control, 15:67-79.
  9. Patton, R. J., Frank, P. M., and Clark, R. (2000). Issues of Fault Diagnosis for Dynamic Systems. SpringerVerlag, Berlin.
  10. Roth, Z. and Baram, Y. (1996). Multidimensional density shaping by sigmoids. IEEE Trans. Neural Networks, 7(5):1291-1298.
  11. Walter, E. and Pronzato, L. (1996). Identification of Parametric Models from Experimental Data. Springer, London.
  12. Xiang-Qun, L. and Zhang, H. Y. (2000). Fault detection and diagnosis of permanent-magnet dc motor based on parameter estimation and neural network. IEEE Trans. Industrial Electronics, 47:1021-1030.
  13. Zhang, J., P. D. R. and Ellis, J. E. (1991). A self-learning fault diagnosis system. Transactions of the Institute of Measurements and Control, 13:29-35.
Download


Paper Citation


in Harvard Style

Patan K., Korbicz J. and Głowacki G. (2007). DC MOTOR FAULT DIAGNOSIS BY MEANS OF ARTIFICIAL NEURAL NETWORKS . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-82-5, pages 11-18. DOI: 10.5220/0001625400110018


in Bibtex Style

@conference{icinco07,
author={Krzysztof Patan and Józef Korbicz and Gracjan Głowacki},
title={DC MOTOR FAULT DIAGNOSIS BY MEANS OF ARTIFICIAL NEURAL NETWORKS},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2007},
pages={11-18},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001625400110018},
isbn={978-972-8865-82-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - DC MOTOR FAULT DIAGNOSIS BY MEANS OF ARTIFICIAL NEURAL NETWORKS
SN - 978-972-8865-82-5
AU - Patan K.
AU - Korbicz J.
AU - Głowacki G.
PY - 2007
SP - 11
EP - 18
DO - 10.5220/0001625400110018