Detection of broken rotor bars of induction motors based on the combination of Hilbert envelope analysis and Shannon entropy

Ahmet Kabul; Abdurrahman Ünsal

doi:10.1515/teme-2020-0066

Article

Detection of broken rotor bars of induction motors based on the combination of Hilbert envelope analysis and Shannon entropy

Ahmet Kabul
Ahmet Kabul received his B. S. in electrical-electronics engineering from Anadolu University, Eskisehir, Turkey, in 2009; his M. S. in electrical-electronics engineering from Dumlupinar University, Kutahya, Turkey, in 2013. He is currently pursuing the Ph. D. degree in electrical-electronics engineering with Kutahya Dumlupinar University. From 2009–2018, he worked as a research assistant in Dumlupinar University. From March 2018–May 2019, he worked in a textile factory as a maintenance engineer. From May 2019 to present, he has been with the Department of Electrical-Electronics Engineering of Burdur Mehmet Akif Ersoy University as a research assistant. His research interests include diagnosing of induction motor failures.
and Abdurrahman Ünsal
Abdurrahman Ünsal received his B. S. from Gazi University (Ankara/Turkey) in 1992, M. S. degree from Oklahoma State University (USA) in 1996, and Ph. D. degree from Oregon State University (USA) in 2001. He is currently working as a Professor at the Department of Electrical and Electronics Engineering of Kütahya Dumlupınar University in Turkey. His research interests are power quality, fault detection and control of electrical machines, renewable energy, and smart grids. He is an IEEE member since 2000.

Published/Copyright: January 7, 2021

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From the journal tm - Technisches Messen Volume 88 Issue 1

Abstract

Broken rotor bar (BRB) is one of the most common fault types of induction motors. One of the common methods to detect the broken rotor bars is the observation of the characteristic sideband frequencies in the stator current. If the motor is lightly loaded, the sideband harmonics are attached to the fundamental frequency of the main supply and the amplitudes of these harmonics are quite low. Therefore, it is difficult to detect the broken rotor bars under light loading conditions by using conventional motor current signature analysis (MCSA) methods. Moreover, in some cases, the sideband harmonics of fundamental frequency may exist although there is no rotor fault in induction motors due to load oscillations. Therefore, there is a risk for false broken rotor bars alarm with the existence of lower amplitude of harmonics. This paper provides an alternative approach for the detection of broken rotor bars by applying Hilbert envelope analysis along with Shannon entropy to stator current signals. The proposed method includes two-stage evaluation system to eliminate false BRB alarms such as detecting sidebands from envelope spectrum and calculating entropy rates from envelope signals. The results are verified experimentally under 25 %, 50 %, 75 % and 100 % loading conditions.

Zusammenfassung

Ein Rotorstabbruch (BRB) ist einer der häufigsten Fehlertypen bei Induktionsmotoren. Eine gängige Methode zur Erkennung gebrochener Rotorstäbe ist die Beobachtung der charakteristischen Seitenbandfrequenzen im Statorstrom. Wenn der Motor leicht belastet ist, liegen die Seitenbandoberwellen nahe der Grundfrequenz und auch deren Amplituden sind gering. Daher ist es schwierig, einen Rotorstabbruch unter geringer Last mit herkömmlichen Motorstromsignatur-Analysetechniken (MCSA) zu erkennen. Darüber hinaus können in seltenen Fällen die Seitenbandoberwellen der Grundfrequenz aufgrund von Lastschwingungen vorhanden sein, obwohl im Induktionsmotor kein Rotorfehler vorliegt. Daher besteht die Gefahr eines falschen Alarms für gebrochene Rotorstäbe bei geringerer Oberwellenamplitude. Dieser Artikel bietet einen alternativen Ansatz zur Erkennung von Rotorstabbrüchen, indem die Hilbert-Hüllkurvenanalyse zusammen mit der Shannon-Entropie auf Statorstromsignale angewendet wird. Um falsche BRB-Alarme auszuschließen umfasst das vorgeschlagene Verfahren ein zweistufiges Bewertungssystem bestehend aus der Erfassung von Seitenbändern des Hüllkurvenspektrums und dem Berechnen von Entropieraten der Hüllkurvensignale. Die Ergebnisse werden experimentell unter 25 %, 50 %, 75 % und 100 % Belastungsbedingungen verifiziert.

Keywords: Broken rotor bars; Hilbert envelope analysis; induction motor; Shannon entropy

Schlagwörter: gebrochene Rotorstäbe; Hilbert-Hüllkurvenanalyse; Induktionsmotor; Shannon-Entropie

Funding source: Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Award Identifier / Grant number: 116E302

Funding statement: This study is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) with a grant number of 116E302.

About the authors

Ahmet Kabul

Ahmet Kabul received his B. S. in electrical-electronics engineering from Anadolu University, Eskisehir, Turkey, in 2009; his M. S. in electrical-electronics engineering from Dumlupinar University, Kutahya, Turkey, in 2013. He is currently pursuing the Ph. D. degree in electrical-electronics engineering with Kutahya Dumlupinar University. From 2009–2018, he worked as a research assistant in Dumlupinar University. From March 2018–May 2019, he worked in a textile factory as a maintenance engineer. From May 2019 to present, he has been with the Department of Electrical-Electronics Engineering of Burdur Mehmet Akif Ersoy University as a research assistant. His research interests include diagnosing of induction motor failures.

Abdurrahman Ünsal

Abdurrahman Ünsal received his B. S. from Gazi University (Ankara/Turkey) in 1992, M. S. degree from Oklahoma State University (USA) in 1996, and Ph. D. degree from Oregon State University (USA) in 2001. He is currently working as a Professor at the Department of Electrical and Electronics Engineering of Kütahya Dumlupınar University in Turkey. His research interests are power quality, fault detection and control of electrical machines, renewable energy, and smart grids. He is an IEEE member since 2000.

References

1. I. Martin-Diaz, D. Morinigo-Sotelo, O. Duque-Perez, R. A. Osornio-Rios and R. J. Romero-Troncoso, “Hybrid algorithmic approach oriented to incipient rotor fault diagnosis on induction motors,” ISA Transactions, 80, pp. 427–438, 2018. doi:10.1016/j.isatra.2018.07.033.Search in Google Scholar PubMed

2. A. V. Bahena, S. E. D. L. Aldaco and J. A. Alquicira, “Simulation for a dual inverter feeding a three-phase open-end winding induction motor: A comparative study of PWM techniques,” European Journal Of Electrical Engineering, 22(1), pp. 13–21, 2020. doi:10.18280/ejee.220102.10.18280/ejee.220102Search in Google Scholar

3. P. Luong, “Smart sensor-based synergistic analysis for rotor bar fault detection of induction motors,” IEEE/ASME Transactions on Mechatronics, 25(2), pp. 1067–1075, 2020. doi:10.1109/TMECH.2020.2970274.Search in Google Scholar

4. I. Chouidira, D. E. Khodja, and S. Chakroune, “Continuous wavelet technique for detection of broken bar faults in induction machine,” Traitement du Signal, 36(2): pp. 171–176, 2019. doi:10.18280/ts.360207.10.18280/ts.360207Search in Google Scholar

5. A. E. Treml, R. A. Flauzino, R. A. Ramos, and G. C. Brito Jr., “Broken rotor bar fault detection in asynchronous machines using vibration analysis,” in: IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, 2019, pp. 1–5.10.1109/PESGM40551.2019.8973827Search in Google Scholar

6. A. G. Garcia-Ramirez, L. A. Morales-Hernandez, R. A. Osornio-Rios, J. P. Benitez-Rangel, A. Garcia-Perez, and R. D. J. Romero-Troncoso, “Fault detection in induction motors and the impact on the kinematic chain through thermographic analysis,” Electric Power Systems Research, 114, pp. 1–9, 2014. doi:10.1016/j.epsr.2014.03.031.Search in Google Scholar

7. G. Mirzaeva, K. I. Saad, and M. G. Jahromi, “Comprehensive diagnostics of induction motor faults based on measurement of space and time dependencies of air gap flux,” IEEE Transactions on Industry Applications, 53(3), pp. 2657–2666, 2017. doi:10.1109/TIA.2016.2628718.Search in Google Scholar

8. Z. Hou, J. Huang, H. Liu, M. Ye, Z. Liu, and J. Yang, “Diagnosis of broken rotor bar fault in open- and closed-loop controlled wye-connected induction motors using zero-sequence voltage,” IET Electric Power Applications, 11(7), pp. 1214–1223, 2017. doi:10.1049/iet-epa.2016.0505.Search in Google Scholar

9. K. Yahia, A. J. M. Cardoso, A. Ghoggal, and S.-E. Zouzou, “Induction motors broken rotor bars diagnosis through the discrete wavelet transform of the instantaneous reactive power signal under time-varying load conditions,” Electric Power Components and Systems, 42(7), pp. 682, 2014. doi:10.1080/15325008.2014.890966.Search in Google Scholar

10. I. Ishkova, and O. Vitek, “Diagnosis of eccentricity and broken rotor bar related faults of induction motor by means of motor current signature analysis,” in: 16th International Scientific Conference on Electric Power Engineering (EPE), Kouty nad Desnou, Czech Republic, 2015, pp. 682–686.10.1109/EPE.2015.7161130Search in Google Scholar

11. H. A. Toliyat, S. Nandi, S. Choi, and H. Meshgin-Kelk, “Electric machines modeling, condition monitoring and fault diagnosis,” CRC Press, 2013, p. 14.Search in Google Scholar

12. K. A. N. Al-Deen, M. E. Karas, A. M. A. Ghaffar, C. Caironi, B. Fruth, and D. Hummes, “Signature analysis as a medium for faults detection in induction motors,” in: International Conference on Computing Sciences and Engineering (ICCSE), Kuwait City, Kuwait, 2018, pp. 984–993.10.1109/ICCSE1.2018.8374224Search in Google Scholar

13. K. N. Gyftakis, A. J. M. Cardoso, and J. A. Antonino-Daviu, “Introducing the filtered park’s and filtered extended park’s vector approach to detect broken rotor bars in induction motors independently from the rotor slots number,” Mechanical Systems and Signal Processing, 93, pp. 30–50, 2017. doi:10.1016/j.ymssp.2017.01.046.Search in Google Scholar

14. G. Trejo-Caballero, H. Rostro-Gonzalez, R. D. J. Romero-Troncoso, C. H. Garcia-Capulin, O. G. Ibarra-Manzano, J. G. Avina-Cervantes, and A. Garcia-Perez, “Multiple signal classification based on automatic order selection method for broken rotor bar detection in induction motors,” Electrical Engineering, 99, pp. 987–996, 2017. doi:10.1007/s00202-016-0463-5.Search in Google Scholar

15. A. Sharma, P. Verma, L. Mathew, and S. Chatterji, “Using motor current analysis for broken rotor bar fault detection in rotary machines,” in: 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2018, pp. 329–334.10.1109/CESYS.2018.8724071Search in Google Scholar

16. G. Singh, and V. N. A. Naikan, “Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis,” Mechanical Systems and Signal Processing, 110, pp. 333–348, 2018. doi:10.1016/j.ymssp.2018.03.001.Search in Google Scholar

17. J. R. Rivera-Guillen, J. J. D. Santiago-Perez, J. P. Amezquita-Sanchez, M. Valtierra-Rodriguez, and R. J. Romero-Troncoso, “Enhanced FFT-based method for incipient broken rotor bar detection in induction motors during the startup transient,” Measurement, 124, pp. 277–285, 2018. doi:10.1016/j.measurement.2018.04.039.Search in Google Scholar

18. D. Camarena-Martinez, M. Valtierra-Rodriguez, J. P. Amezquita-Sanchez, D. Granados-Lieberman, R. J. Romero-Troncoso, and A. Garcia-Perez, “Shannon entropy and k-means method for automatic diagnosis of broken rotor bars in induction motors using vibration signals,” Shock and Vibration, 2016(2), pp. 1–10, 2016. doi:10.1155/2016/4860309.Search in Google Scholar

19. I. Harzelli, A. Menacer, and T. Ameid, “A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor,” Journal of Ambient Intelligence and Humanized Computing, 10(5), pp. 1–20, 2019. doi:10.1007/s12652-019-01307-0.Search in Google Scholar

20. L. Salah, G. Adel, K. Khaled, and B. Ahmed, “A comparative investigation between the MCSA method and the Hilbert transform for broken rotor bar fault diagnostics in a closed-loop three-phase induction motor,” UPB Sci. Bull., Series C 81(3), pp. 209–226, 2019.Search in Google Scholar

21. B. Asad, T. Vaimann, A. Kallaste, and A. Rassolkin, “Hilbert transform an effective replacement of Park’s vector modulus for the detection of rotor faults,” in: Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM), Kärdla, Estonia, 2019, pp. 1–4.10.1109/PQ.2019.8818227Search in Google Scholar

22. M. Irfan, N. Saad, R. Ibrahim, V. S. Asirvadam, and M. Magzoub, “An online fault diagnosis system for induction motors via instantaneous power analysis,” Tribology Transactions, 60(4), pp. 592–604, 2017. doi:10.1080/10402004.2016.1190043.Search in Google Scholar

23. M. Khodapanah, A. F. Zobaa, and M. Abbod, “Estimating power factor of induction motors at any loading conditions using Support Vector Regression (SVR),” Electrical Engineering, 100, pp. 2579–2588, 2018. doi:10.1007/s00202-018-0723-7.Search in Google Scholar

24. V. Rajinikanth, S. C. Satapathy, S. L. Fernandes, and S. Nachiappan, “Entropy based segmentation of tumor from brain MR images – A study with teaching learning based optimization,” Pattern Recognition Letters, 94, pp. 87–95, 2017. doi:10.1016/j.patrec.2017.05.028.Search in Google Scholar

25. F. Villecco, and A. Pellegrino, “Entropic measure of epistemic uncertainties in multibody system models by axiomatic design,” Entropy, 19(7), 2017. doi:10.3390/e19070291.Search in Google Scholar

26. S. Pan, T. Han, A. C. C. Tan, and T. R. Lin, “Fault diagnosis system of induction motors based on multiscale entropy and support vector machine with mutual information algorithm,” Shock and Vibration, 2016(7), pp. 1–12, 2016. doi:10.1155/2016/5836717.Search in Google Scholar

27. I. Aydin, M. Karakose, and E. Akin, “A new method for early fault detection and diagnosis of broken rotor bars,” Energy Conversion and Management, 52(4), pp. 1790–1799, 2011. doi:10.1016/j.enconman.2010.11.018.Search in Google Scholar

28. E. Cabal-Yepez, A. A. Fernandez-Jaramillo, A. Garcia-Perez, R. J. Romero-Troncoso, and J. M. Lozan-Garcia, “Real-time condition monitoring on VSD-Fed induction motors through statistical analysis and synchronous speed observation,” International Transactions on Electrical Energy Systems, 25(8), pp. 1657–1672, 2015. doi:10.1002/etep.1938.Search in Google Scholar

29. X. Zhang, Y. Liang, J. Zhou, and Y. Zang, “A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM,” Measurement, 69: 164–179, 2015. doi:10.1016/j.measurement.2015.03.017.Search in Google Scholar

30. M. Hernandez-Vargas, E. Cabal-Yepez, and A. Garcia-Perez, “Real-time SVD-based detection of multiple combined faults in induction motors,” Computers & Electrical Engineering, 40(7): pp. 2193–2203, 2014. doi:10.1016/j.compeleceng.2013.12.020.Search in Google Scholar

31. A. Medoued, A. Lebaroud, A. Laifa, and D. Sayad, “Classification of induction machine faults using time frequency representation and particle swarm optimization,” Journal of Electrical Engineering and Technology, 9(1): 170–177, 2014. doi:10.5370/JEET.2014.9.1.170.Search in Google Scholar

32. P. Gomez-Gil, J. Rangel-Magdaleno, J. M. Ramirez-Cortes, E. Garcia-Trevino, and I. Cruz-Vega, “Intelligent identification of induction motor conditions at several mechanical loads,” in: IEEE International Instrumentation and Measurement Technology Conference Proceedings, Taipei, Taiwan, 2016, pp. 1–5.10.1109/I2MTC.2016.7520396Search in Google Scholar

33. R. A. Patel, and B. R. Bhalja, “Condition monitoring and fault diagnosis of induction motor using Support Vector Machine,” Electric Power Components and Systems, 44(6), 683–692, 2016. doi:10.1080/15325008.2015.1131762.Search in Google Scholar

34. C. Yang, T.-J. Kang, D. Hyun, S. B. Lee, J. A. Antonino-Daviu, and J. Pons-Llinares, “Reliable detection of induction motor faults under the rotor axial air duct influence,” IEEE Transactions on Industry Applications, 50(4), pp. 2493–2502, 2014. doi:10.1109/TIA.2013.2297448.Search in Google Scholar

35. J. Kim, S. Shin, S. B. Lee, N. Gyftakis, M. Drif, and A. J. M. Cardoso, “Power spectrum-based detection of induction motor rotor faults for immunity to false alarms,” IEEE Transactions on Energy Conversion, 30(3), pp. 1123–1132, 2015. doi:10.1109/TEC.2015.2423315.Search in Google Scholar

36. M. Nemec, V. Ambrozic, R. Fiser, D. Nedeljkovic, and K. Drobnic, “Induction motor broken rotor bar detection based on rotor flux angle monitoring,” Energies, 12(5), p. 794, 2019. doi:10.3390/en12050794.Search in Google Scholar

37. M. R. Mehrjou, N. Mariun, M. Karami, N. Misron, and M. A. M. Radzi, “Broken rotor bar detection in LS-PMSMs based on statistical features analysis of start-up current envelope,” in: IEEE 3rd International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), Kuala Lumpur, Malaysia, 2015, pp. 1–6.10.1109/ICSIMA.2015.7559033Search in Google Scholar

38. N. Jiang, and J. Li, “Adaptive speech enhancement algorithm based on Hilbert-Huang Transform,” Ingenierie des Systemes d’Information, 24(1), pp. 57–60, 2019. doi:10.18280/isi.240108.10.18280/isi.240108Search in Google Scholar

39. C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, 27(3), pp. 379–423, 1948. doi:10.1002/j.1538-7305.1948.tb01338.x.Search in Google Scholar

40. A. Unsal, and A. Kabul, “Evaluation of rotor faults of induction motors by statistical analysis methods,” Journal of Polytechnic, 20(2), pp. 283–289, 2017.Search in Google Scholar

41. H. H. Bafroui, and A. Ohadi, “Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions,” Neurocomputing, 133, pp. 437–445, 2014. doi:10.1016/j.neucom.2013.12.018.Search in Google Scholar

42. H. Cui, L. Zhang, R. Kang, and X. Lan, “Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method,” Journal of Loss Prevention in the Process Industries, 22(6), pp. 864–867, 2009. doi:10.1016/j.jlp.2009.08.012.Search in Google Scholar

43. E. Cabal-Yepez, M. Valtierra-Rodriguez, R. J. Romero-Troncoso, A. Garcia-Perez, R. A. Osornio-Rios, H. Miranda-Vidales, and R. Alvarez-Salas, “FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors,” Mechanical Systems and Signal Processing, 30, pp. 123–130, 2012. doi:10.1016/j.ymssp.2012.01.021.Search in Google Scholar

44. D. Sonmez, S. Seker, and M. Gokasan, “Entropy-based fault detection approach for motor vibration signals under accelerated aging process,” Journal of Vibroengineering, 14(3), pp. 1263–1277, 2012.Search in Google Scholar

Received: 2020-08-11

Accepted: 2020-12-05

Published Online: 2021-01-07

Published in Print: 2021-01-26

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https://doi.org/10.1515/teme-2020-0066

Keywords for this article

Broken rotor bars; Hilbert envelope analysis; induction motor; Shannon entropy