Rouaibia Reda | Arbaoui Fayçal | Bahi Tahar
OPEN ACCESS
This paper describes the monitoring and diagnosis of eccentricity fault in variable speed induction motor drive. The used control technique is the indirect field oriented control (IFOC) fuzzy logic based controller to ensure the robust speed regulation and to compensate the fault effects. The variable speed induction motor can be affected by various kinds of faults. Airgap eccentricity is one of the major defects occurring in such electric drives; its detection could be useful for preventing potential catastrophic failures. A dynamic model taking into account the faults is proposed based on the approach of magnetically coupled coils to simulate the behavior of eccentricity faults in induction motor. This work presents two approaches for diagnosis and detection of eccentricity faults and evaluation of their severity based on monitoring of the stator current signals, using Park vector method and discrete wavelet transform (DWT) with different approaches to distinguish healthy as well as faulty conditions of the machine. The obtained simulation results via the proposed technique allow detection and diagnosis of eccentricity fault and identify their severity.
Fuzzy logic controller, Diagnosis, Induction motor, Mixed eccentricity, Discrete wavelet transform.
1. S. Chacko, C.N. Bhende, S. Jain, R.K. Nema, Modeling and simulation of field oriented control induction motor drive and influence of rotor resistance variations on its performance, 2016, Electrical and Electronics Engineering: An International Journal, vol. 5, no 1.
2. K. Hasse, Dynamic of adjustable speed drives with converter-fed squirrel cage induction motors, 1968, (Germany) Ph. D. Dissertation, Darmstadt, Technische.
3. F. Blaschke, The principle of field orientation as applied to the new transvector closed-loop control system for rotating-field machines, 1972, Siemens Rev, vol. 34, no. 3, pp. 217-220.
4. M. Akar, Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method, 2013, Mechanical Systems and Signal Processing, vol. 34, no. 1-2, pp. 173-182.
5. H. Talhaoui, A. Menacer, A. Kessal, R. Kechida, Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis, 2014, ISA Transactions, vol. 53, no. 5, pp. 1639-1649.
6. T. Ameid, A. Menacer1, H. Talhaoui I. Harzelli1, Broken rotor bar fault diagnosis using fast Fourier transform applied to field-oriented control induction machine: simulation and experimental study, 2017, Int. J Adv Manuf. Technol., vol. 92, no. 1-4, pp. 917-928.
7. A. Chaouch, M. Harir, A. Bendiabdellah, P. Remus, Instantaneous Power Spectrum Analysis To Detect Mixed Eccentricity Fault In Saturated Squirrel Cage Induction Motor,3rd international Conference on Automation , Control, Engineering and Computer Science 2016.
8. I. Ouachtouk, S. Elhani, S. guedira, k. dahil, l Sadiki, Advanced Model of Squirrel Cage Induction Machine for Broken Rotor Bars Fault Using Multi Indicators, 2016, power engineering and electrical engineering, vol. 14, no. 5.
9. J.R. Magdaleno, H.P. Barreto, J.R. Cortes, R.M. Caporal, I.C.Vega, Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT , 2016, Hindawi Publishing Corporation, vol. 2016, ID 3530464.
10. B. Bessam, A. Menacer, M. Boumehraz, H. Cherif, Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor, 2015, Int. J Syst Assur Eng Manag, vol. 8, pp. 478-488.
11. A. Jawadekar, S. Paraskar, S. Jadhav, G. Dhole, Artificial neural network-based induction motor fault classifier using continuous wavelet transform, 2014,Systems Science & Control Engineering, vol. 2, pp. 684–690 .
12. W. Laala, S-E. Zouzou, S.Guedidi, Induction motor broken rotor bars detection using fuzzy logic: experimental research, 2013, Int. J Syst Assur Eng Manag, vol. 5, no. 3, pp. 329–336.
13. El H. El Bouchikhi, V Choqueuse, M Benbouzid, Condition Monitoring of Induction Motors Based on Stator Currents Demodulation ,2015,International Review of Electrical engineering, vol. 10, no 6, pp. 704-715.
14. R. Kechida, A. Menacer, DWT Wavelet Transform for the Rotor Bars Faults Detection in Induction Motor, Electric Power and Energy Conversion Systems (EPECS), 2nd International Conference 2011.
15. K. Yahia, A.J.M. Cardoso, A. Ghoggal, S.E. Zouzou, Induction motors air-gap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating, 2014, conditions, ISA Transactions, vol. 53(2), pp. 603-611.
16. N. Bessous, S. E. Zouzou, W. Bentrah, S. Sbaa, M. Sahraoui, Diagnosis of bearing defects in induction motors using discrete wavelet transform, Int. J Syst Assur Eng Manag, 2015 page 1.
17. Jawad Faiz and S.M.M. Moosavi, Eccentricity fault detection – From induction Machines to DFIG, A review Renewable and Sustainable Energy Reviews, 2016, vol. 55, no. c, pp. 169-179.
18. W. Wroński, M. Sułowicz, A. Dziechciarz, Dynamic and Static Eccentricity Detection in Induction Motors in Transient States, 2015, Technical Transactions Electrical Engineering, vol. 112, no. 2-E, pp. 171-194.
19. A. Intesar, A. Manzar, I. Kashif, M. Shuja Khan, Detection of Eccentricity Faults in Machine Using Frequency Spectrum Technique, February, 2011, International Journal of Computer and Electrical Engineering, vol. 3, no. 1.
20. N. Mehla, R. Dahiya, Detection of Bearing Faults of Induction Motor Using Park’s Vector Approach ,2010, International Journal of Engineering and Technology, vol. 2(4), pp. 263-266.
21. C.da. Costa, M. Kashiwagi, M. H.Mathias, Rotor failure detection of induction motors by wavelet transform and Fourier transform in non-stationary condition, 2015, Case Studies in Mechanical Systems and Signal Processing, vol. 1, pp.15-26.
22. K. M. Siddiqui, K. Sahay, V.K. Giri.Early, Diagnosis of Bearing Fault in the Inverter Driven Induction Motor by Wavelet Transform , International Conference on Circuit, Power and Computing Technologies, 2016.