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Sonje, Deepak M, Munje, Ravindra. (1404). Simultaneous Bearing Faults Detection in Three Phase Induction Motor Based on Feature Fusion Method and Random Forest Algorithm. سامانه مدیریت نشریات علمی, 13(4), 269-276. doi: 10.22098/joape.2024.13044.1987
Deepak M Sonje; Ravindra Munje. "Simultaneous Bearing Faults Detection in Three Phase Induction Motor Based on Feature Fusion Method and Random Forest Algorithm". سامانه مدیریت نشریات علمی, 13, 4, 1404, 269-276. doi: 10.22098/joape.2024.13044.1987
Sonje, Deepak M, Munje, Ravindra. (1404). 'Simultaneous Bearing Faults Detection in Three Phase Induction Motor Based on Feature Fusion Method and Random Forest Algorithm', سامانه مدیریت نشریات علمی, 13(4), pp. 269-276. doi: 10.22098/joape.2024.13044.1987
Sonje, Deepak M, Munje, Ravindra. Simultaneous Bearing Faults Detection in Three Phase Induction Motor Based on Feature Fusion Method and Random Forest Algorithm. سامانه مدیریت نشریات علمی, 1404; 13(4): 269-276. doi: 10.22098/joape.2024.13044.1987

Simultaneous Bearing Faults Detection in Three Phase Induction Motor Based on Feature Fusion Method and Random Forest Algorithm

Journal of Operation and Automation in Power Engineering
مقاله 2، دوره 13، شماره 4، اسفند 2025، صفحه 269-276    اصل مقاله (1.14 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.13044.1987
نویسندگان
Deepak M Sonje* 1؛ Ravindra Munje2
1Electrical Engineering Department RHSCOEMS & R, Nashik, India.
2Electrical Engineering Department, KKWCOE, Nashik, India.
چکیده
Fault detection and classification (FDC) is a vital area in the health monitoring of three-phase induction machines. According to the failure survey of three three-phase induction machines, bearing-related faults cause a percentage of motor failures in the range of almost 41-50% which is very significant. These faults may occur one or multiple at a time in the bearing. With a well-designed fault detection method, failure of the motor can be reduced and productivity can also be increased. This paper proposes the simultaneous bearing fault detection and classification in three three-phase induction machine using the combination of feature fusion method and intelligent random forest (RF) algorithm. The paper contributes in two folds. In the first part of the paper, the performance of traditional methods such as vibration and current analysis is tested in which statistical parameters obtained from current and vibration signals are passed separately to the intelligent random forest classifier. In the second part of the paper, statistical parameters obtained from current and vibration signals are fused together and used as inputs to the RF classifier. The accuracy and various other performance measures are calculated and based on experimental results; a remarkably high detection/classification performance is achieved.
کلیدواژه‌ها
Bearing fault؛ fault detection and classification؛ induction motor؛ random forest
مراجع
  1. V. N. Ghate and S. V. Dudul, “Cascade neural-network-based fault classifier for three-phase induction motor,” IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1555–1563, 2010.
  2. Y. Liu and A. M. Bazzi, “A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art,” ISA Trans., vol. 70, pp. 400–409, 2017.
  3. T. Garcia-Calva, D. Morinigo-Sotelo, V. Fernandez-Cavero, and R. Romero-Troncoso, “Early detection of faults in induction motorsa review,” Energies, vol. 15, no. 21, p. 7855, 2022.
  4. S. Patidar and P. K. Soni, “An overview on vibration analysis techniques for the diagnosis of rolling element bearing faults,” Int. J. Eng. Trends Technol. (IJETT), vol. 4, no. 5, pp. 1804–1809, 2013.
  5. D. Neupane and J. Seok, “Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review,” IEEE Access, vol. 8, pp. 93155–93178, 2020.
  6. J. Guo, H. Zhang, D. Zhen, Z. Shi, F. Gu, and A. D. Ball, “An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-gaussian noise suppression,” Meas., vol. 151, p. 107240, 2020.
  7. S. Suh, H. Lee, J. Jo, P. Lukowicz, and Y. O. Lee, “Generative oversampling method for imbalanced data on bearing fault detection and diagnosis,” Appl. Sci., vol. 9, no. 4, p. 746, 2019.
  8. H. Wang, W. Yue, S. Wen, X. Xu, H.-D. Haasis, M. Su, P. Liu, S. Zhang, and P. Du, “An improved bearing fault detection strategy based on artificial bee colony algorithm,” CAAI Trans. Intell. Technol., vol. 7, no. 4, pp. 570–581, 2022.
  9. J. Zarei, M. A. Tajeddini, and H. R. Karimi, “Vibration analysis for bearing fault detection and classification using an intelligent filter,” Mechatron., vol. 24, no. 2, pp. 151–157, 2014.
  10. M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, “Bearing fault detection by a novel conditionmonitoring scheme based on statistical-time features and neural networks,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3398–3407, 2012.
  11. G. Yu, C. Li, and J. Zhang, “A new statistical modeling and detection method for rolling element bearing faults based on alpha–stable distribution,” Mech. Syst. Signal Process., vol. 41, no. 1-2, pp. 155–175, 2013.
  12. D. Zhu, Q. Gao, D. Sun, Y. Lu, and S. Peng, “A detection method for bearing faults using null space pursuit and s transform,” Signal Process., vol. 96, pp. 80–89, 2014.
  13. G. Maruthi and V. Hegde, “Application of mems accelerometer for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor,” IEEE Sens. J., vol. 16, no. 1, pp. 145–152, 2015.
  14. R. R. Schoen, T. G. Habetler, F. Kamran, and R. Bartfield, “Motor bearing damage detection using stator current monitoring,” IEEE Trans. Ind. Appl., vol. 31, no. 6, pp. 1274–1279, 1995.
  15. J. Zarei and J. Poshtan, “An advanced park’s vectors approach for bearing fault detection,” Tribol. Int., vol. 42, no. 2, pp. 213–219, 2009.
  16. A. Picot, Z. Obeid, J. Régnier, S. Poignant, O. Darnis, and P. Maussion, “Statistic-based spectral indicator for bearing fault detection in permanent-magnet synchronous machines using the stator current,” Mech. Syst. Signal Process., vol. 46, no. 2, pp. 424–441, 2014.
  17. L. Frosini, C. Harlis¸ca, and L. Szabó, “Induction machine bearing fault detection by means of statistical processing of the stray flux measurement,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1846–1854, 2014.
  18. V. Ghate and S. Dudul, “Svm based fault classification of three phase induction motor,” Indian J. Sci. Technol., pp. 32–35, 2009.
  19. S. Singh, A. Kumar, and N. Kumar, “Motor current signature analysis for bearing fault detection in mechanical systems,” Procedia Mater. Sci., vol. 6, pp. 171–177, 2014.
  20. W. Zhou, T. G. Habetler, and R. G. Harley, “Stator currentbased bearing fault detection techniques: A general review,” in 2007 IEEE Int. Symp. Diagn. Electr. Mach. Power Electron. Drives, pp. 7–10, IEEE, 2007.
  21. V. C. Leite, J. G. B. da Silva, G. F. C. Veloso, L. E. B. da Silva, G. Lambert-Torres, E. L. Bonaldi, and L. E.
    d. L. de Oliveira, “Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1855–1865, 2014.
  22. A. Widodo and B.-S. Yang, “Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors,” Expert Syst. Appl., vol. 33, no. 1, pp. 241–250, 2007.
  23. A. Soualhi, K. Medjaher, and N. Zerhouni, “Bearing health monitoring based on hilbert–huang transform, support vector machine, and regression,” IEEE Trans. Instrum. Meas., vol. 64, no. 1, pp. 52–62, 2014.
  24. V. Ghate and S. Dudul, “Induction machine fault detection using support vector machine based classifier,” WSEAS Trans. Syst., vol. 8, no. 5, pp. 591–603, 2009.
  25. V. Ghate and S. Dudul, “Induction machine fault detection using support vector machine based classifier,” WSEAS Trans. Syst., vol. 8, no. 5, pp. 591–603, 2009.
  26. B. Samanta and K. Al-Balushi, “Artificial neural network based fault diagnostics of rolling element bearings using time-domain features,” Mech. Syst. Signal Process., vol. 17, no. 2, pp. 317–328, 2003.
  27. M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, “Bearing fault detection by a novel conditionmonitoring scheme based on statistical-time features and neural networks,” IEEE Trans. Ind. Electron., vol. 60, no. 8, pp. 3398–3407, 2012.
  28. J. Zarei, “Induction motors bearing fault detection using pattern recognition techniques,” Expert Syst. Appl., vol. 39, no. 1, pp. 68–73, 2012.
  29. B. Samanta, K. Al-Balushi, and S. Al-Araimi, “Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection,” Eng. Appl. Artif. Intell., vol. 16, no. 7-8, pp. 657–665, 2003.
  30. A. Kimothi, A. Singh, V. Sugumaran, and M. Amarnath, “Fault diagnosis of helical gearbox through vibration signals using wavelet features, j48 decision tree and random forest classifiers,” Indian J. Sci. Technol., vol. 9, p. 33, 2016.
  31. A. Choudhary, T. Mian, S. Fatima, and B. Panigrahi, “Passive thermography based bearing fault diagnosis using transfer learning with varying working conditions,” IEEE Sens. J., vol. 23, no. 5, pp. 4628–4637, 2022.
  32. B. Brusamarello, J. C. C. da Silva, K. de Morais Sousa, and G. A. Guarneri, “Bearing fault detection in three-phase induction motors using support vector machine and fiber bragg grating,” IEEE Sens. J., vol. 23, no. 5, pp. 4413–4421, 2022.
  33. M. Khanjani and M. Ezoji, “Electrical fault detection in threephase induction motor using deep network-based features of thermograms,” Meas., vol. 173, p. 108622, 2021.
  34. M. Jafarboland and S. Mousavi, “Investigation of unbalanced magnetic force in permanent magnet brushless dc machines with diametrically asymmetric winding,” J. Oper. Autom. Power Eng., vol. 6, no. 2, pp. 255–267, 2018.
  35. M. Nikpayam, M. Ghanbari, A. Esmaeli, and M. Jannati, “Vector control methods for star-connected three-phase induction motor drives under the open-phase failure,” J. Oper. Autom. Power Eng., vol. 10, no. 2, pp. 155–164, 2022.
  36. S. Chaihang, N. Chamnongwai, and P. Chansri, “Phase shiftbased fault detection of induction motor using iot system,” in 2022 Int. Conf. Power Energy Innovations (ICPEI), pp. 1–4, IEEE, 2022.
  37. M. Geravandi and H. M. CheshmehBeigi, “Stray load losses determination methods of induction motors-a review,” in 2022 30th Int. Conf. Electr. Eng. (ICEE), pp. 1033–1038, IEEE, 2022.
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