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Comparison of Fault Detection Techniques for Induction Motors

by Amir Ahmed Qazi, Jawaid Daudpoto, Salman Ahmed Shaikh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 38
Year of Publication: 2021
Authors: Amir Ahmed Qazi, Jawaid Daudpoto, Salman Ahmed Shaikh
10.5120/ijca2021921778

Amir Ahmed Qazi, Jawaid Daudpoto, Salman Ahmed Shaikh . Comparison of Fault Detection Techniques for Induction Motors. International Journal of Computer Applications. 183, 38 ( Nov 2021), 13-19. DOI=10.5120/ijca2021921778

@article{ 10.5120/ijca2021921778,
author = { Amir Ahmed Qazi, Jawaid Daudpoto, Salman Ahmed Shaikh },
title = { Comparison of Fault Detection Techniques for Induction Motors },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2021 },
volume = { 183 },
number = { 38 },
month = { Nov },
year = { 2021 },
issn = { 0975-8887 },
pages = { 13-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number38/32178-2021921778/ },
doi = { 10.5120/ijca2021921778 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:00.085369+05:30
%A Amir Ahmed Qazi
%A Jawaid Daudpoto
%A Salman Ahmed Shaikh
%T Comparison of Fault Detection Techniques for Induction Motors
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 38
%P 13-19
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the era of twenty-first century, induction motor plays a dominant role in industrial processes and essentially run out 40 to 50 % of total energy demand. Accordingly, their safety, durability, and efficiency are of major concern. Faults developing in induction motor necessitates significant consideration as they eradicate its operation and reduce the mean life. In this research, the most widely used MCSA that captures stator current signatures and acceleration-based vibration diagnosis techniques are practically investigated employing low-cost sensors. Moreover,the comparative analysis is performed to find an effective method for detection of faults, efficiently and persuade motor safety and reliable operation.

References
  1. Nunez J, Velazquez L, Hernandez L, Troncoso R, Osornio-Rios R, “Low-cost thermographic analysis for bearing fault detection on induction motors”, Journal of Science and Industrial Research, (2016). 75:412–415.
  2. Gangsar, P., & Tiwari, R, “Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms”, Mechanical Systems and Signal Processing (Elsevier), (2017). 94, 464–481.
  3. Hadden, T., Jiang, J. W., Bilgin, B., Yinye Yang, Sathyan, A., Dadkhah, H., &Emadi, A, “A Review of Shaft Voltages and Bearing Currents in EV and HEV Motors”, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.
  4. Hmida, M. A. & Braham, A, “An on-line condition monitoring system for incipient fault detection in double-cage induction motor”, IEEE Transactions on Instrumentation and Measurement, 2018. 67, 1850-1858.
  5. Glowacz, A., Glowacz, W., Glowacz, Z., &Kozik, J, “Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals”, Measurement (Elsevier), 2018. 113, 1–9.
  6. Dash, R. N., Sahu, S., Panigrahi, C. K., &Subudhi, B, “Condition monitoring of induction motors: — A review”, 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016.
  7. Othman MS, Nuawi MZ, Mohamed R, “Experimental comparison of vibration and acoustic emission signal analysis using kurtosis-based methods for induction motor bearing condition monitoring”, PrzegladElektrotechniczny, 2016. 92(11):208–212.
  8. Othman MS, Nuawi MZ, Mohamed R, “Vibration and acoustic emission signal monitoring for detection of induction motor bearing fault”, International Journal of Engineering Research & Technology (IJERT), 2015. 4(5).
  9. Duan, Z., Wu, T., Guo, S., Shao, T., Malekian, R., & Li, Z, “Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review”, The International Journal of Advanced Manufacturing Technology, 2018. 96, 803–819.
  10. Malla, C., &Panigrahi, I, “Review of Condition Monitoring of Rolling Element Bearing Using Vibration Analysis and Other Technique”, Journal of Vibration Engineering & Technologies (Springer), 2019. 7, 407–414.
  11. MisraR., Shinghal K., Saxena A., Agarwal A, “Industrial Motor Bearing Fault Detection Using Vibration Analysis”, International Conference on Intelligent Computing and Smart Communication 2019. Algorithms for Intelligent Systems. Springer, Singapore, 2020. 827-839.
  12. Singh, S., & Kumar, N, “Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring”, IEEE Transactions on Industrial Informatics, 2016. 13(3), 1341–1349.
  13. Mousavi S, Kar NC, Timusk M, “A novel parallel modelling-wavelet based mechanical fault detection using stator current signature of induction machine under variable load conditions”, Journal of Electrical Engineering & Electronic Technology, (2017). 6(2):2–9.
  14. Glowacz A, Glowacz Z, “Diagnosis of the three-phase induction motor using thermal imaging”, Infrared Physics & Technology, (2017). 81:7–16.
  15. Ola E. Hassan, MotazAmer, Ahmed K. Abdelsalam, Barry W. Williams, “Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review”, IET Electric Power Applications, 2018. 12(7), 895 – 907.
  16. Nikhil, Mathew, L., & Sharma, A, “Various Indices for Diagnosis of Air-gap Eccentricity Fault in Induction Motor-A Review”, IOP Conference Series: Materials Science and Engineering, 2017. 331.
  17. Rangel-Magdaleno, J., Peregrina-Barreto, H., Ramirez-Cortes, J., Morales-Caporal, R. & Cruz-Vega, I, “Vibration analysis of partially damaged rotor bar in induction motor under different load condition using DWT”, 2016. Shock and Vibration.
  18. Delgado-Arredondo, P. A., Morinigo-Sotelo, D., Osornio-Rios, R. A., Avina-Cervantes, J. G., Rostro-Gonzalez, H., & Romero-Troncoso, R. de J, “Methodology for fault detection in induction motors via sound and vibration signals”, Mechanical Systems and Signal Processing (Elsevier), (2017). 83, 568–589.
  19. Prakasam, K. and Ramesh, S, “Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis”, Circuits and Systems, 2016. 7, 2651-2662.
  20. Singhal A, Khandekar MA, “Bearing fault detection in induction motor using motor current signature analysis”, Int J Adv Res Electr Electron InstrumEng, 2013. 2(7):3258–3264.
  21. Huo, Z.; Zhang, Y.; Sath, R.; Shu, L, “Self-adaptive fault diagnosis of roller bearings using infrared thermal image”, In Proceedings of the 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Beijing, China.
  22. A. Widodo and B.-S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis”, Mechanical systems and signal processing, 2007. 21(6), 2560–2574.
  23. Pires, V. F., Martins, J., Pires, A. & Rodrigues, L, “Induction motor broken bar fault detection based on MCSA, MSCSA and PCA: A comparative study”, 2016 10th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), IEEE, 2016. 298-303.
  24. Jin, X., Cheng, F., Peng, Y., Qiao, W. &Qu, L, “A comparative study on Vibration-and current-based approaches for drivetrain gearbox fault diagnosis” 2016 IEEE Industry Applications Society Annual Meeting, IEEE, 2016. 1-8.
  25. Mitra, S. &Koley, C, “Different measurement techniques for detection of bearing faults in industrial actuators—comparative study” 2017 IEEE Calcutta Conference (CALCON), IEEE, 2017. 412-417.
  26. Kumar, P., Hati, A.S,“Review on Machine Learning Algorithm Based Fault Detection in Induction Motors”. Arch Computat Methods Eng, 2021. 28, 1929–1940.
  27. Gundewar, S.K., Kane, P.V,“Condition Monitoring and Fault Diagnosis of Induction Motor”, J. Vib. Eng. Technol, 2021. 9, 643–674.
Index Terms

Computer Science
Information Sciences

Keywords

Motor Current Signature Analysis (MSCA) Fast Fourier Transform (FFT) Condition Monitoring (CM).