DOI: https://doi.org/10.14710/ijred.7.1.43-52

Automatic and Online Detection of Rotor Fault State

1Department of Electrical Engineering, University 20 Aout 1955-Skikda. B.P.26 Route El-Hadaiek, Skikda 21000, Algeria

2Department of Mechanical Engineering, University 20 Aout 1955-Skikda. B.P.26 Route El-Hadaiek, Skikda 21000, Algeria

Published: 18 Feb 2018.
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Abstract

In this work, we propose a new and simple method to insure an online and automatic detection of faults that affect induction motor rotors. Induction motors now occupy an important place in the industrial environment and cover an extremely wide range of applications. They require a system installation that monitors the motor state to suit the operating conditions for a given application. The proposed method is based on the consideration of the spectrum of the single-phase stator current envelope as input of the detection algorithm. The characteristics related to the broken bar fault in the frequency domain extracted from the Hilbert Transform is used to estimate the fault severity for different load levels through classification tools. The frequency analysis of the envelope gives the frequency component and the associated amplitude which define the existence of the fault. The clustering of the indicator is chosen in a two-dimensional space by the fuzzy c mean clustering to find the center of each class. The distance criterion, the K-Nearest Neighbor (KNN) algorithm and the neural networks are used to determine the fault type. This method is validated on a 5.5-kW induction motor test bench.

Article History: Received July 16th 2017; Received: October 5th 2017; Accepted: Januari 6th 2018; Available online

How to Cite This Article: Ouanas, A., Medoued, A., Haddad, S., Mordjaoui, M., and Sayad, D. (2017) Automatic and online Detection of Rotor Fault State. International Journal of Renewable Energy Development, 7(1), 43-52.

http://dx.doi.org/10.14710/ijred.7.1.43-52
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Keywords: broken bar fault; Fuzzy c mean clustering; Hilbert Transform; neural network; KNN; envelope

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Section: Original Research Article
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  1. Aydin, I., Karakose, M., & Akin, E. (2007, May). A simple and efficient method for fault diagnosis using time series data mining. In Electric Machines, & Drives Conference, 2007. IEMDC'07. IEEE International (Vol. 1, pp. 596-600). IEEE
  2. Ballal, M. S., Khan, Z. J., Suryawanshi, H. M., & Sonolikar, R. L. (2007). Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Transactions on Industrial Electronics, 54(1), 250-258
  3. Benakcha, M., Benalia, L., Ameur, F., & Tourqui, D. E. (2017). Control of Dual Stator Induction Generator integrated in Wind Energy Conversion System
  4. Benbouzid, M. E. H. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE transactions on industrial electronics, 47(5), 984-993
  5. Bonnett, A. H., & Yung, C. (2008). Increased efficiency versus increased reliability. IEEE Industry Applications Magazine, 14(1).,
  6. Boukra, T., Lebaroud, A., & Clerc, G. (2013). Statistical and neural-network approaches for the classification of induction machine faults using the ambiguity plane representation. IEEE Transactions on Industrial Electronics, 60(9), 4034-4042
  7. Bezdek.J.C (1981). Pattern recognition with fuzzy objective function algoritms. New York: Plenum Press;
  8. Culbert, I., & Rhodes, W. (2005, September). Using current signature analysis technology to reliably detect cage winding defects in squirrel cage induction motors. In Petroleum and Chemical Industry Conference, 2005. Industry Applications Society 52nd Annual (pp. 95-101). IEEE
  9. Dunn JC. ( 1978), A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybernet 3,32–57
  10. Ghorbanian, V., & Faiz, J. (2015). A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes. Mechanical Systems and Signal Processing, 54, 427-456
  11. Jimenez, G. A., Munoz, A. O., & Duarte-Mermoud, M. A. (2007). Fault detection in induction motors using Hilbert and Wavelet transforms. Electrical Engineering, 89(3), 205-220
  12. Kia, S. H., Henao, H., & Capolino, G. A. (2007). A high-resolution frequency estimation method for three-phase induction machine fault detection. IEEE Transactions on Industrial Electronics, 54(4), 2305-2314
  13. Kia, S. H., Henao, H., & Capolino, G. A. (2009). Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation. IEEE Transactions on Industry Applications, 45(4), 1395-1404
  14. King, F. W. (2009). Hilbert transforms (Vol. 1). New York: Cambridge University Press
  15. Laala, W., Zouzou, S. E., & Guedidi, S. (2014). Induction motor broken rotor bars detection using fuzzy logic: experimental research. International Journal of System Assurance Engineering and Management, 5(3), 329-336
  16. Lebaroud, A., & Medoued, A. (2013). Online computational tools dedicated to the detection of induction machine faults. International Journal of Electrical Power, & Energy Systems, 44(1), 752-757
  17. Matić, D., Kulić, F., Pineda-Sánchez, M., & Kamenko, I. (2012). Support vector machine classifier for diagnosis in electrical machines: Application to broken bar. Expert Systems with Applications, 39(10), 8681-8689
  18. McLaughlin, D. V., & Pearce, J. M. (2013). Progress in indium gallium nitride materials for solar photovoltaic energy conversion. Metallurgical and Materials Transactions A, 44(4), 1947-1954
  19. Medoued, A., Mordjaoui, M., Soufi, Y., & Sayad, D. (2016). Induction machine bearing fault diagnosis based on the axial vibration analytic signal. International Journal of Hydrogen Energy, 41(29), 12688-12695
  20. Medoued, A., Lebaroud, A., Laifa, A., & Sayad, D. (2014). Classification of induction machine faults using time frequency representation and particle swarm optimization. Journal of Electrical Engineering and Technology, 9(1), 170-177
  21. Munshi, A., & Sampath, W. (2016). CdTe Photovoltaics for Sustainable Electricity Generation. J. Electron. Mater
  22. Ordaz-Moreno, A., de Jesus Romero-Troncoso, R., Vite-Frias, J. A., Rivera-Gillen, J. R., & Garcia-Perez, A. (2008). Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for FPGA implementation. IEEE Transactions on Industrial Electronics, 55(5), 2193-2202
  23. Puche-Panadero, R., Pineda-Sanchez, M., Riera-Guasp, M., Roger-Folch, J., Hurtado-Perez, E., & Perez-Cruz, J. (2009). Improved resolution of the MCSA method via Hilbert transform, enabling the diagnosis of rotor asymmetries at very low slip. IEEE Transactions on Energy Conversion, 24(1), 52-59
  24. Rahman, M. M., Baky, M. A. H., & Islam, A. S. (2017). Electricity from Wind for Off-Grid Applications in Bangladesh: A Techno-Economic Assessment. International Journal of Renewable Energy Development, 6(1), 55
  25. Sadeghian, A., Ye, Z., & Wu, B. (2009). Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Transactions on Instrumentation and Measurement, 58(7), 2253-2263
  26. Sreedharan, S., Ongsakul, W., Singh, J. G., & Mahapatra, S. S. (2011). Development of PSO-based robust controller for maximising wind penetration. International Journal of Renewable Energy Technology, 3(1), 58-78
  27. Tavner, P. J. (2008). Review of condition monitoring of rotating electrical machines. IET Electric Power Applications, 2(4), 215-247
  28. Toke, D. (2015). Renewable Energy Auctions and Tenders: How good are they?. International Journal of Sustainable Energy Planning and Management, 8, 43-56
  29. Wang, J., Liu, S., Gao, R. X., & Yan, R. (2012). Current envelope analysis for defect identification and diagnosis in induction motors. Journal of Manufacturing Systems, 31(4), 380-387
  30. Ye, Z., Wu, B., & Zargari, N. (2000). Online mechanical fault diagnosis of induction motor by wavelet artificial neural network using stator current. In Industrial Electronics Society, 2000. IECON 2000. 26th Annual Conference of the IEEE (Vol. 2, pp. 1183-1188). IEEE

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