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Application of Moving Horizon Parameter Estimator in Fault Diagnosis of Broken Bars in Induction Motor

by Chouiref Houda, Boussaid Boumedyen, Abdelkrim M. Naceur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 17
Year of Publication: 2012
Authors: Chouiref Houda, Boussaid Boumedyen, Abdelkrim M. Naceur
10.5120/7863-1125

Chouiref Houda, Boussaid Boumedyen, Abdelkrim M. Naceur . Application of Moving Horizon Parameter Estimator in Fault Diagnosis of Broken Bars in Induction Motor. International Journal of Computer Applications. 50, 17 ( July 2012), 19-23. DOI=10.5120/7863-1125

@article{ 10.5120/7863-1125,
author = { Chouiref Houda, Boussaid Boumedyen, Abdelkrim M. Naceur },
title = { Application of Moving Horizon Parameter Estimator in Fault Diagnosis of Broken Bars in Induction Motor },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 17 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number17/7863-1125/ },
doi = { 10.5120/7863-1125 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:48:32.871943+05:30
%A Chouiref Houda
%A Boussaid Boumedyen
%A Abdelkrim M. Naceur
%T Application of Moving Horizon Parameter Estimator in Fault Diagnosis of Broken Bars in Induction Motor
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 17
%P 19-23
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The fault diagnosis and prediction of electrical machines and drives has become of importance because of its great influence on the operational continuation of many industrial processes. Correct diagnosis and early detection of incipient faults avoids harmful, sometimes devastative, consequences. In this work, on the basis of a model of an induction motor we study the approach for the detection of broken rotor bars problem using residual generators based in moving horizon estimator of the rotor resistance. Which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. Simulation and experimental results show the effectiveness of the proposed method for broken rotor bar detection in induction motors.

References
  1. Akhenak, A. , Chadli, M. , Maquin, D. and Ragot, J. 2004. State estimation of uncertain multiple model with unknown inputs. 43rd IEEE Conference on Decision and Control, CDC'04, Atlantis, Paradise Island, Bahamas, December 14-17, 2004.
  2. Asokan, A. and Sivakumar, D. 2009. Fault detection and diagnosis for three-tank system using robust residual generator, Indian Journal of Science and Technology 2(2009), 23-29.
  3. Athamena, B. and Abbassi, H. 2003. Fault Detection and Isolation Using Hybrid Parameter Estimation and Fuzzy Logic Residual Evaluation, International journal of computing and informatics, 27(2003), 29–37.
  4. Faiz, J. and Ebrahimi, B. A. 2008. New Pattern for Detecting Broken Rotor Bars in Induction Motors During Start-Up, IEEE TRANSACTIONS ON MAGNETICS, 44 (2008), 4673-4683.
  5. Gonzalez, H. , Flaus, J. and Acuna, G. 2003. Moving horizon state estimation with global convergence using interval techniques: application to biotechnological processes, Journal of Process Control, 13 (2003), 325–336.
  6. Karamia, F. and Poshtana, J. P. M. 2010. Detection of broken rotor bars in induction motors using nonlinear Kalman filters, ISA Transactions, 49 (2010), 189–195.
  7. Kuhl, P. , Diehlc, M. K. T. , Schloderb, J. and Bock, H. 2010. A real-time algorithm for moving horizon state and parameter estimation, Journal of Computers and Chemical Engineering, 35 (2010), 71-83.
  8. Maaziz, M. K. and Mendes, E. B. P. 2002. A new nonlinear multivariable control strategy of induction motors, International Journal of Control Engineering Practice, 10 (2002), 605–613.
  9. Olivier-Maget, N. , Hetreux, G. , Le Lann, J. M. , Le Lann, M. V. 2007. Robust fault detection for the hybrid dynamic systems, 4th IFAC Conference on Management and Control of Production and Logistics (MCPL), Sibiu - Romania, 27-30 September, 2007.
  10. Raissi, T. , Ramdani, N. and Candau, Y. 2005. Bounded error moving horizon state estimator for non-linear continuous-time systems: application to a bioprocess system, Journal of Process Control, 15 (2005), 537-545.
  11. Simani, S. , Fantuzi, C. and Patton, R. J. 2003. Model-based fault diagnosis in dynamic systems using identification techniques, Springer: London, 2003, 282pp. ISBN 1-85233-685-4.
  12. Treetronga, J. , Sinhaa, J. , Gub, F. and Ballb, A. 2009. Bispectrum of stator phase current for fault detection of induction motor, ISA Transactions,48 (2009), 378–382.
  13. Zaidi, S. , Naceri, F. and Abdessamed, R. A. 2007. New Non Linear Multivariable Control of Induction Motor, International Journal of Electrical and Power Engineering, 1 (2007), 300-306.
Index Terms

Computer Science
Information Sciences

Keywords

Fault diagnosis Moving horizon estimator Induction motors Residual generators Rotor resistance

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