SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH

Oriane Le Pocher, Eric Duviella, Karine Chuquet

Abstract

This paper focuses on the sensor fault detection of a hydraulic channel used for navigation. This system has the particularities to have large scale dimension, without slope, with several inputs and ouputs, and thus difficult to be modelled according to classical modelling methods. For recent years, it was equipped with level sensors in order to have better knowwledge of its behavior, to detect its state online and thus improve its management. However, level sensors are subjected to measurement or transmission errors, setting errors, and quick or slow drifts. In order to detect these sensor errors, a classification approach is proposed. It appears adapted to the fault detection of large scale hydraulic systems without model. The classification approach is used on data measured from 2006 to 2009. The first results and analysis show that the classification method is effective for addressing the problem of sensor fault detection.

References

  1. Akhenak, A., Chadli, M., Ragot, J., and Maquin, D. (2004). State estimation of uncertain multiple model with unknown inputs. In 43rd IEEE Conference on Decision and Control, Atlantis, Bahamas, page 35633568.
  2. Bedjaoui, N. and Weyer, E. (2010). Algorithms for leak detection, estimation, isolation and localization in open water channels. Control Engineering Practice, In Press.
  3. Chow, V. T., Maidment, D. R., and Mays, L. W. (1998). Applied Hydrology. McGraw-Hill.
  4. Eltoft, T. and de Figueiredo, R. (1998). A nez neural network for cluster-detection-and-labeling. IEEE Trans. Neural Networks, 9:1021-1035.
  5. Frank, P. M., Ding, S. X., and KCipper-Seligcr, B. (2000). Current developments in the theory of fdi. In SAFEPROCESS00, Budapest, Hungary, pages 16-27.
  6. Gertler, J. (1998). Fault Detection and Diagnosis in Engineering Systems. Dekker.
  7. Hartert, L., Mouchaweh, M. S., and Billaudel, P. (2010). Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior. IGI.
  8. Lecoeuche, S., Lurette, C., and Lalot, S. (2004). New supervision architecture based on on-line modeling of non-stationary data. Neural Computing and Applications Journal, 13:323-338.
  9. Mouchaweh, M. S., Devillez, A., Lecolier, G., and Billaudel, P. (2002). Recursive learning in real time using fuzzy pattern matching. Mathematics and Computers in Simulation, 60:209-216.
  10. Su, M.-C. and Liu, Y.-C. (2005). A new approach to clustering data with arbitrary shapes. Pattern Recognition, 38:1887-1901.
  11. Traore, M., Duviella, E., and Lecoeuche, S. (2009). Dynamical clustering technique to estimate the probability of the failure occurrence of process subjected to slow degradation. In ICINCO, Milan, Italy, pages 636-643.
  12. Weihua, L., Harigopal, R., and Sirish, S. (2003). Subspace identification of continuous time models for process fault detection and isolation. Journal of Process Control, 13:407-421.
  13. Xie, L., Soh, Y. C., and de Souzi, C. E. (1994). Robust kalman filtering for uncertain discrete-time systems. IEEE Transaction on Automatic Control, 93:131 01314.
Download


Paper Citation


in Harvard Style

Le Pocher O., Duviella E. and Chuquet K. (2011). SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-74-4, pages 382-387. DOI: 10.5220/0003571603820387


in Bibtex Style

@conference{icinco11,
author={Oriane Le Pocher and Eric Duviella and Karine Chuquet},
title={SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2011},
pages={382-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003571603820387},
isbn={978-989-8425-74-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH
SN - 978-989-8425-74-4
AU - Le Pocher O.
AU - Duviella E.
AU - Chuquet K.
PY - 2011
SP - 382
EP - 387
DO - 10.5220/0003571603820387