Broken Rotor Bar Fault Detection in Induction Motor Based on Spectral Analysis

Öz

Three-phase induction motors are among the most frequently used motors in industrial areas due to their simple structure, low cost, power specifications, etc. Electric energy consumption from these motors accounts for 68% of the energy used by all motors. The faults that occur in these motors over time decrease motor efficiency and result in significant energy consumption. In this study, a new method based on spectral subtraction (SS) was suggested for determining broken rotor bar (BRB) faults in these motors. The traditional method of motor current signature analysis via Fast Fourier Transform (FFT) is hard to use to diagnose BRB faults because the sideband characteristics used as a fault indicator are also seen in the healthy state at low amplitude levels. In the proposed fault detection method, the FFT of both healthy case and faulty case current signals were calculated and then the SS signal is obtained by subtracting the FFT of the healthy motor from the faulty motor for each time step. In the residual SS signal, fault detection was performed by examining the amplitude levels of the harmonic component of the BRB fault. Experimental results indicate that BRB faults can be successfully detected in squirrel-cage rotor induction motors using the suggested method.

Anahtar Kelimeler

• Broken rotor bar
• Induction motor
• Spectral subtraction
• Energy-saving
• Induction motor energy consumption.

Kaynakça

1. [1] S. M. Lu, “A review of high-efficiency motors: Specification, policy, and technology,” Renewable and Sustainable Energy Reviews, vol. 59, pp. 1–12, Jun. 2016, doi: 10.1016/j.rser.2015.12.360.
2. [2] E. C. Bortoni, J. V. Bernardes, P. V. V. da Silva, V. A. D. Faria, and P. A. V. Vieira, “Evaluation of manufacturers strategies to obtain high-efficient induction motors,” Sustainable Energy Technologies and Assessments, vol. 31, pp. 221–227, Feb. 2019, doi: 10.1016/J.SETA.2018.12.022.
3. [3] “Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis - Hamid A. Toliyat, Subhasis Nandi, Seungdeog Choi, Homayoun Meshgin-Kelk - Google Books.” Accessed: Feb. 28, 2024. [Online]. Available: https://books.google.com.tr/books?hl=en&lr=&id=JDSp3hCo9sUC&oi=fnd&pg=PP1&dq=info:D4vov8eNTewJ:scholar.google.com&ots=dk1uPvwfRZ&sig=AkxPS9U6Cqvsb08w--YHBZKixQM&redir_esc=y#v=onepage&q&f=false
4. [4] S. Nandi, H. A. Toliyat, and X. Li, “Condition monitoring and fault diagnosis of electrical motors - A review,” IEEE Transactions on Energy Conversion, vol. 20, no. 4, pp. 719–729, Dec. 2005, doi: 10.1109/TEC.2005.847955.
5. [5] M. Akar and I. Cankaya, “Broken rotor bar fault detection in inverter-fed squirrel cage induction motors using stator current analysis and fuzzy logic,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 20, no. 7, pp. 1077–1089, Jan. 2012, doi: 10.3906/elk-1102-1050.
6. [6] M. Akar, “Detection of a static eccentricity fault in a closed loop driven induction motor by using the angular domain order tracking analysis method,” Mech Syst Signal Process, vol. 34, no. 1–2, pp. 173–182, Jan. 2013, doi: 10.1016/J.YMSSP.2012.04.003.
7. [7] Ç. BAKIR, “Different Induction Motor Faults by New Proposed Random Forest Method,” Balkan Journal of Electrical and Computer Engineering, vol. 11, no. 4, pp. 380–386, Dec. 2023, doi: 10.17694/BAJECE.1283336.
8. [8] F. Filippetti, M. Martelli, G. Franceschini, and C. Tassoni, “Development of expert system knowledge base to on-line diagnosis of rotor electrical faults of induction motors,” Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), vol. 1992-January, pp. 92–99, 1992, doi: 10.1109/IAS.1992.244459.

9. [9] M. Abd-el-Malek, A. K. Abdelsalam, and O. E. Hassan, “Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform,” Mech Syst Signal Process, vol. 93, pp. 332–350, Sep. 2017, doi: 10.1016/J.YMSSP.2017.02.014.
10. [10] Z. Wang, J. Yang, H. Li, D. Zhen, Y. Xu, and F. Gu, “Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis,” Energies 2019, Vol. 12, Page 3279, vol. 12, no. 17, p. 3279, Aug. 2019, doi: 10.3390/EN12173279.
11. [11] N. Mehala and R. Dahiya, “Motor Current Signature Analysis and its Applications in Induction Motor Fault Diagnosis,” INTERNATIONAL JOURNAL OF SYSTEMS APPLICATIONS, ENGINEERING & DEVELOPMENT, vol. 2, no. 1, pp. 29–35, 2007.
12. [12] R. R. Schoen and T. G. Habetler, “A NEW METHOD OF CURRENT-BASED CONDITION MONITORING IN INDUCTION MACHINES OPERATING UNDER ARBITRARY LOAD CONDITIONS,” Electric Machines And Power Systems, vol. 25, no. 2, pp. 141–152, Feb. 1997, doi: 10.1080/07313569708955729.
13. [13] A. Bellini, F. Filippetti, G. Franceschini, C. Tassoni, and G. B. Kliman, “Quantitative evaluation of induction motor broken bars by means of electrical signature analysis,” IEEE Trans Ind Appl, vol. 37, no. 5, pp. 1248–1255, Sep. 2001, doi: 10.1109/28.952499.
14. [14] W. T. Thomson and M. Fenger, “Current signature analysis to detect induction motor faults,” IEEE Industry Applications Magazine, vol. 7, no. 4, pp. 26–34, Jul. 2001, doi: 10.1109/2943.930988.
15. [15] C. Delmotte-Delforge, H. Hénao, G. Ekwe, P. Brochet, and G. A. Capolino, “Comparison of two modeling methods for induction machine study: Application to diagnosis,” COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 22, no. 4, pp. 891–908, 2003, doi: 10.1108/03321640310482887/FULL/PDF.
16. [16] P. Kołodziejek and E. Bogalecka, “Broken rotor bar impact on sensorless control of induction machine,” COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 28, no. 3, pp. 540–555, 2009, doi: 10.1108/03321640910940837/FULL/PDF.
17. [17] S. Guedidi, S. E. Zouzou, W. Laala, K. Yahia, and M. Sahraoui, “Induction motors broken rotor bars detection using MCSA and neural network: Experimental research,” International Journal of System Assurance Engineering and Management, vol. 4, no. 2, pp. 173–181, Mar. 2013, doi: 10.1007/S13198-013-0149-6/FIGURES/23.
18. [18] H. Keskes and A. Braham, “Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis,” IEEE Trans Industr Inform, vol. 11, no. 5, pp. 1059–1066, Oct. 2015, doi: 10.1109/TII.2015.2462315.
19. [19] O. Abdi Monfared, A. Doroudi, and A. Darvishi, “Diagnosis of rotor broken bars faults in squirrel cage induction motor using continuous wavelet transform,” COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 38, no. 1, pp. 167–182, Jan. 2019, doi: 10.1108/COMPEL-11-2017-0487/FULL/PDF.
20. [20] B. Asad, T. Vaimann, A. Rassõlkin, A. Kallaste, and A. Belahcen, “Review of Electrical Machine Diagnostic Methods Applicability in the Perspective of Industry 4.0,” Electrical, Control and Communication Engineering, vol. 14, no. 2, pp. 108–116, Dec. 2018, doi: 10.2478/ECCE-2018-0013.
21. [21] B. Asad, T. Vaimann, A. Kallaste, and A. Belahcen, “Harmonic spectrum analysis of induction motor with broken rotor bar fault,” 2018 IEEE 59th Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2018 - Proceedings, 2018, doi: 10.1109/RTUCON.2018.8659842.
22. [22] K. C. Deekshit Kompella, M. Venu Gopala Rao, and R. Srinivasa Rao, “Bearing fault detection in a 3 phase induction motor using stator current frequency spectral subtraction with various wavelet decomposition techniques,” Ain Shams Engineering Journal, vol. 9, no. 4, pp. 2427–2439, Dec. 2018, doi: 10.1016/J.ASEJ.2017.06.002.
23. [23] O. Abdi Monfared, A. Doroudi, and A. Darvishi, “Diagnosis of rotor broken bars faults in squirrel cage induction motor using continuous wavelet transform,” COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 38, no. 1, pp. 167–182, Jan. 2019, doi: 10.1108/COMPEL-11-2017-0487/FULL/PDF.
24. [24] M. G. Armaki and R. Roshanfekr, “A new approach for fault detection of broken rotor bars in induction motor based on support vector machine,” Proceedings - 2010 18th Iranian Conference on Electrical Engineering, ICEE 2010, pp. 732–738, 2010, doi: 10.1109/IRANIANCEE.2010.5506976.
25. [25] S. V. Vaseghi, “Advanced Signal Processing and Digital Noise Reduction,” Advanced Signal Processing and Digital Noise Reduction, 1996, doi: 10.1007/978-3-322-92773-6.
26. [26] S. F. Boll, “Suppression of Acoustic Noise in Speech Using Spectral Subtraction,” IEEE Trans Acoust, vol. 27, no. 2, pp. 113–120, 1979, doi: 10.1109/TASSP.1979.1163209.
27. [27] P. Shi, Z. Chen, Y. Vagapov, and Z. Zouaoui, “A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor,” Mech Syst Signal Process, vol. 42, no. 1–2, pp. 388–403, Jan. 2014, doi: 10.1016/J.YMSSP.2013.09.002.
28. [28] A. Stefani, A. Yazidi, C. Rossi, F. Filippetti, D. Casadei, and G. A. Capolino, “Doubly fed induction machines diagnosis based on signature analysis of rotor modulating signals,” IEEE Trans Ind Appl, vol. 44, no. 6, pp. 1711–1721, 2008, doi: 10.1109/TIA.2008.2006322.
29. [29] S. Günal, D. G. Ece, and Ö. N. Gerek, “Zaman bölgesinde akim analiziyle i̇ndüksiyon motoru hata tespiti,” 2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009, pp. 488–491, 2009, doi: 10.1109/SIU.2009.5136439.
30. [30] E. H. El Bouchikhi, V. Choqueuse, and M. E. H. Benbouzid, “Current frequency spectral subtraction and its contribution to induction machines’ bearings condition monitoring,” IEEE Transactions on Energy Conversion, vol. 28, no. 1, pp. 135–144, 2013, doi: 10.1109/TEC.2012.2227746.
31. [31] A. Kabul and A. Ünsal, “Diagnosis of simultaneous broken rotor bars and static eccentricity faults of induction motors by analyzing stator current and vibration signals,” Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 36, no. 4, pp. 2011–2023, 2021, doi: 10.17341/gazimmfd.697785.