Article Sidebar

Download
PDF
Statistic
Read Counter : 567 Download : 374

Downloads

Download data is not yet available.

Main Article Content

Abstract

 In this paper, a neuro-fuzzy fault diagnosis scheme is presented and its ability to detect and isolate sensor faults in an induction motor is assessed. This fault detection and isolation (FDI) approach relies on a combination of neural modelling and fuzzy logic techniques which can deal effectively with nonlinear dynamics and uncertainties. It is based on a two step neural network procedure: a first neural network is used for residual generation and a second fuzzy neural network performs residual evaluation. Simulation results are given to demonstrate the efficiency of this FDI approach.

 

Keywords

Fuzzy logic Induction motor Neural networks Sensor fault detection and isolation

Article Details

How to Cite
Benloucif, M. L. (2011). Neuro-Fuzzy Sensor Fault Diagnosis of an Induction Motor. The Journal of Engineering Research [TJER], 8(1), 53–60. https://doi.org/10.24200/tjer.vol8iss1pp53-60
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Alexandru, M., Combastel, C. and Gentil, S., 2000, "Diagnostic Decision using Recurrent Neural Networks," Proc. IFAC Safeprocess 2000, Budapest, Hungary.
  2. Babuska, R., 1998, “Fuzzy Modeling for Control,” Kluwer Academic Publishers.
  3. Benloucif, M.L. and Staroswiecki, M., 2002, "Fault Diagnosis using a Robust Estimation Method," Proc. International Conference CIFA-2002, Nantes, France.
  4. Benloucif, M.L. and Mehennaoui, L., 2002, "A Mixed Analytical Neuro-Fuzzy Approach for Fault Diagnosis," Proc. 2ND Instrumentation and Measurement in Petroleum Applications Conference IMPAC-2002, Boumerdès, Algeria.
  5. Benloucif, M.L. and Mehennaoui, L., 2005, "A Fuzzy Neural Scheme for Fault Diagnosis," Proc. International Computer Systems and Information Technology Conference ICSIT'05, Algiers, Algeria.
  6. Benloucif, M.L. and Balaska, H., 2006, "Robust Fault Detection for an Induction Machine," 7th World Automation Congress- WAC 2006, Budapest, Hungary.
  7. Chen, Y.M. and Lee, M.L., 2002, "Neural Networks Based Scheme for System Failure Detection and Diagnosis," Mathematics and Computers in Simulation, 58, pp. 101-109.
  8. Evsukoff, A., Combastel, C. and Gentil, S., 1999, "Qualitative Reasoning and Neural Network Decision Procedures for Fault Detection and Isolation," Proc. 14th IFAC World Congress, Beijing, China.
  9. Frank, P.M., 1990, "Fault Diagnosis in Dynamic Systems using Analytical and Knowledge Based Redundancy - A Survey and Some New Results," Automatica, 26, pp. 459-474.
  10. Frank, P.M., 1994, "Application of Fuzzy Logic to Process Supervision and Fault Diagnosis," Proc. IFAC Safeprocess 94, Espoo, Finland.
  11. Garcia, E. and Frank, P.M., 1997, "Deterministic Nonlinear Observer Based Approaches to Fault Diagnosis: A survey," Control Eng. Practice, vol. 5(5), pp. 663-670.
  12. Isermann, R., 1998, "On Fuzzy Logic Applications for Automatic Control, Supervision and Fault Diagnosis," IEEE Trans. on Systems, Man and Cybernetics, Vol. 28(2).
  13. Jiang, B., Staroswiecki, M. and Cocquempot, V., 2001, "Robust Observer Based Fault Diagnosis for a Class of Nonlinear Systems with Uncertainty," Proc. 40th IEEE Conference on Decision and Control, CDC'01, Orlando, USA.
  14. Mamdani, E. and Assilian, S., 1995, "An Experiment in linguistic Synthesis with Fuzy Logic Controller," Int. J. Man-Machine Studies, 7(1).
  15. Norgaard, M., Ravn, O., Poulsen, N.K.. and Hansen, L.K., 2000, "Neural Network for Modelling and Control of Dynamic Systems," Springer Verlag, London.
  16. Patton, R. and Chen, J., 1997, "Observer-Based Fault Detection and Isolation: Robustness and Applications," Control Eng. Practice, Vol. 5(5), pp. 671-682.
  17. Schneider, H. and Frank, P.M., 1996, "Observer Based Supervision and Fault Detection in Robots using Nonlinear and Fuzzy Logic Residual Evaluation," IEEE Trans. on Control System Technology, Vol. 4(3), pp. 274-282.
  18. Simani, S. and Fantuzzi, C., 2002, "Neural Networks for Fault Diagnosis and Identification of Industrial Processes," Proc. of the 10th European Symposium on Artificial Neural Networks, Bruges , Belgium.
  19. Takagi, T. and Sugeno, M., 1985 "Fuzzy Identification of Systems and Applications to Modelling and Control," IEEE Trans. On system, Man and Cybernetics, Vol. 15(1).
  20. Theilliol, D., Sauter, D. and Vela Valdes, L.G., 1997, "Integration of Qualitative and Quantitative Methods for Fault Detection and Isolation," Proc. IFAC Safeprocess 97, Hull, U.K.
  21. Uppal, F.J., Patton, R.J. and Palade, V., 2002, "Neuro- Fuzzy Based Fault Diagnosis Applied to an Electro- Pneumatic Valve," 15th IFAC World Congress, Barcelona, Spain.