Wavelet packet analysis for rotor bar breakage in an inverter induction motor

Authors

DOI:

https://doi.org/10.20998/2074-272X.2023.3.01

Keywords:

squirrel cage induction motors, rotor broken bar, continuous wavelet transform, discrete wavelet transform

Abstract

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.

Author Biographies

O. Z. I. Abu Ibaid, University of M’Sila

PhD, Department of Electrical Engineering, Electrical Engineering Laboratory

S. Belhamdi, University of M’Sila

Professor, Department of Electrical Engineering, Electrical Engineering Laboratory

M. Abid, University of Tiaret

PhD, Department of Electrical Engineering, L2GEGI Laboratory

S. Chakroune, University of M’Sila

Professor, Department of Electrical Engineering, Electrical Engineering Laboratory

S. Mouassa, University of Bouira

Senior Lecturer, Department of Electrical Engineering

Z. S. Al-Sagar, Baqubah Technical Institute, Middle Technical University

Associate Professor, Department of Renewable Energy

References

Dehina W., Boumehraz M., Kratz F. Diagnosis of Rotor and Stator Faults by Fast Fourier Transform and Discrete Wavelet in Induction Machine. 2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 2018, pp. 1-6. doi: https://doi.org/10.1109/CISTEM.2018.8613311.

Rohan A., Kim S.H. Fault Detection and Diagnosis System for a Three-Phase Inverter Using a DWT-Based Artificial Neural Network. The International Journal of Fuzzy Logic and Intelligent Systems, 2016, vol. 16, no. 4, pp. 238-245. doi: https://doi.org/10.5391/IJFIS.2016.16.4.238.

Bessam B., Menacer A., Boumehraz M., Cherif H. DWT and Hilbert Transform for Broken Rotor Bar Fault Diagnosis in Induction Machine at Low Load. Energy Procedia, 2015, vol. 74, pp. 1248-1257. doi: https://doi.org/10.1016/j.egypro.2015.07.769.

Amanuel T., Ghirmay A., Ghebremeskel H., Ghebrehiwet R., Bahlibi W. Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor. Journal of Electronics and Informatics, 2021, vol. 3, no. 1, pp. 61-76. doi: https://doi.org/10.36548/jei.2021.1.006.

Menacer A., Moreau S., Benakcha A., Said M.S.N. Effect of the Position and the Number of Broken Bars on Asynchronous Motor Stator Current Spectrum. 2006 12th International Power Electronics and Motion Control Conference, 2006, pp. 973-978. doi: https://doi.org/10.1109/EPEPEMC.2006.4778526.

Talhaoui H., Ameid T., Kessal A. Energy eigenvalues and neural network analysis for broken bars fault diagnosis in induction machine under variable load: experimental study. Journal of Ambient Intelligence and Humanized Computing, 2022, vol. 13, no. 5, pp. 2651-2665. doi: https://doi.org/10.1007/s12652-021-03172-2.

Ince T. Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks. Electrical Engineering, 2019, vol. 101, no. 2, pp. 599-608. doi: https://doi.org/10.1007/s00202-019-00808-7.

Talhaoui H., Ameid T., Aissa O., Kessal A. Wavelet packet and fuzzy logic theory for automatic fault detection in induction motor. Soft Computing, 2022, vol. 26, no. 21, pp. 11935-11949. doi: https://doi.org/10.1007/s00500-022-07028-5.

Abid M., Laribi S., Larbi M., Allaoui T. Diagnosis and localization of fault for a neutral point clamped inverter in wind energy conversion system using artificial neural network technique. Electrical Engineering & Electromechanics, 2022, no. 5, pp. 55-59. doi: https://doi.org/10.20998/2074-272X.2022.5.09.

Kompella K.C.D., Mannam V.G.R., Rayapudi S.R. DWT based bearing fault detection in induction motor using noise cancellation. Journal of Electrical Systems and Information Technology, 2016, vol. 3, no. 3, pp. 411-427. doi: https://doi.org/10.1016/j.jesit.2016.07.002.

Saidi L., Fnaiech F., Henao H., Capolino G.-A., Cirrincione G. Diagnosis of broken-bars fault in induction machines using higher order spectral analysis. ISA Transactions, 2013, vol. 52, no. 1, pp. 140-148. doi: https://doi.org/10.1016/j.isatra.2012.08.003.

Bouzida A., Touhami O., Abdelli R. Rotor Fault Diagnosis in Three Phase Induction Motors Using the Wavelet Transform. International Conference on Control, Engineering & Information Technology (CEIT'13). Proceedings Engineering & Technology, 2013, vol. 1, pp. 186-191. Available at: http://ipco-co.com/PET_Journal/presented%20papers/095.pdf (accessed 24 July 2022).

Dehina W., Boumehraz M., Kratz F. Diagnosis and Detection of Rotor Bars Faults in Induction Motor Using HT and DWT Techniques. 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), 2021, pp. 109-115. doi: https://doi.org/10.1109/SSD52085.2021.9429381.

Hussein A.M., Obed A.A., Zubo R.H.A., Al-Yasir Y.I.A., Saleh A.L., Fadhel H., Sheikh-Akbari A., Mokryani G., Abd-Alhameed R.A. Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach. Electronics, 2022, vol. 11, no. 8, art. no. 1253. doi: https://doi.org/10.3390/electronics11081253.

Cherif H., Menacer A., Bessam B., Kechida R. Stator inter turns fault detection using discrete wavelet transform. 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2015, pp. 138-142. doi: https://doi.org/10.1109/DEMPED.2015.7303681.

Hassen K., Braham A., Zied L. Diagnosis of broken rotor bar in induction machines using pitch synchronous wavelet transform. 4th International Conference on Power Engineering, Energy and Electrical Drives, 2013, pp. 896-901. doi: https://doi.org/10.1109/PowerEng.2013.6635729.

Eltabach M., Charara A., Zein I. A Comparison of External and Internal Methods of Signal Spectral Analysis for Broken Rotor Bars Detection in Induction Motors. IEEE Transactions on Industrial Electronics, 2004, vol. 51, no. 1, pp. 107-121. doi: https://doi.org/10.1109/TIE.2003.822083.

Didier G., Ternisien E., Caspary O., Razik H. Fault detection of broken rotor bars in induction motor using a global fault index. IEEE Transactions on Industry Applications, 2006, vol. 42, no. 1, pp. 79-88. doi: https://doi.org/10.1109/TIA.2005.861368.

Abu Ibaid O.Z.I., Belhamdi S., Abid M., Chakroune S. Diagnosis of rotor faults of asynchronous machine by spectral analysis of stator currents. 5th International Aegean Symposiums on Innovation Technologies & Engineering, February 25-26, 2022, pp. 102-111. Available at: https://www.aegeanconference.com/_files/ugd/614b1f_1930a1802a034e389c403c987ca63644.pdf (accessed 24 July 2022).

Chouidira I., Khodja D., Chakroune S. Fuzzy Logic Based Broken Bar Fault Diagnosis and Behavior Study of Induction Machine. Journal Européen Des Systèmes Automatisés, 2020, vol. 53, no. 2, pp. 233-242. doi: https://doi.org/10.18280/jesa.530210.

Souad L., Bendiabdallah Youcef M., Samir M., Boukezata. Use of neuro-fuzzy technique in diagnosis of rotor faults of cage induction motor. 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B), 2017, pp. 1-4. doi: https://doi.org/10.1109/ICEE-B.2017.8192148.

Abdelhak G., Sid Ahmed B., Djekidel R. Fault diagnosis of induction motors rotor using current signature with different signal processing techniques. Diagnostyka, 2022, vol. 23, no. 2, pp. 1-9. doi: https://doi.org/10.29354/diag/147462.

Chow T.W.S., Hai S. Induction Machine Fault Diagnostic Analysis With Wavelet Technique. IEEE Transactions on Industrial Electronics, 2004, vol. 51, no. 3, pp. 558-565. doi: https://doi.org/10.1109/TIE.2004.825325.

Mohamed M.A., Hassan M.A.M., Albalawi F., Ghoneim S.S.M., Ali Z.M., Dardeer M. Diagnostic Modelling for Induction Motor Faults via ANFIS Algorithm and DWT-Based Feature Extraction. Applied Sciences, 2021, vol. 11, no. 19, art. no. 9115. doi: https://doi.org/10.3390/app11199115.

Kechida R., Menacer A. DWT wavelet transform for the rotor bars faults detection in induction motor. 2011 2nd International Conference on Electric Power and Energy Conversion Systems (EPECS), 2011, pp. 1-5. doi: https://doi.org/10.1109/EPECS.2011.6126825.

Kompella K.C.D., Mannam V.G.R., Rayapudi S.R. DWT based bearing fault detection in induction motor using noise cancellation. Journal of Electrical Systems and Information Technology, 2016, vol. 3, no. 3, pp. 411-427. doi: https://doi.org/10.1016/j.jesit.2016.07.002.

Behim M., Merabet L., Saad S. Detection and Classification of Induction Motor Faults Using DWPD and Artificial Neural Network: Case of Supply Voltage Unbalance and Broken Rotor Bars. EasyChair Preprint no. 7756. Available at: https://easychair.org/publications/preprint_download/TrtH (accessed 24 July 2022).

Mehrjou M.R., Mariun N., Karami M., Noor S.B.M., Zolfaghari S., Misron N., Kadir M.Z.A.A., Radzi M.A.M., Marhaban M.H. Wavelet-Based Analysis of MCSA for Fault Detection in Electrical Machine. In Wavelet Transform and Some of Its Real-World Applications. InTech., 2015. doi: https://doi.org/10.5772/61532.

Cusido J., Romeral L., Ortega J.A., Rosero J.A., Espinosa A.G. Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition. IEEE Transactions on Industrial Electronics, 2008, vol. 55, no. 2, pp. 633-643. doi: https://doi.org/10.1109/TIE.2007.911960.

Amanuel T., Ghirmay A., Ghebremeskel H., Ghebrehiwet R., Bahlibi W. Comparative Analysis of Signal Processing Techniques for Fault Detection in Three Phase Induction Motor. Journal of Electronics and Informatics, 2021, vol. 3, no. 1, pp. 61-76. doi: https://doi.org/10.36548/jei.2021.1.006.

Martinez-Herrera A.L., Ferrucho-Alvarez E.R., Ledesma-Carrillo L.M., Mata-Chavez R.I., Lopez-Ramirez M., Cabal-Yepez E. Multiple Fault Detection in Induction Motors through Homogeneity and Kurtosis Computation. Energies, 2022, vol. 15, no. 4, art. no. 1541. doi: https://doi.org/10.3390/en15041541.

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Published

2023-04-23

How to Cite

Abu Ibaid, O. Z. I., Belhamdi, S., Abid, M., Chakroune, S., Mouassa, S., & Al-Sagar, Z. S. (2023). Wavelet packet analysis for rotor bar breakage in an inverter induction motor. Electrical Engineering & Electromechanics, (3), 3–11. https://doi.org/10.20998/2074-272X.2023.3.01

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Section

Electrical Machines and Apparatus