On-board diagnostics method for detecting bearing defects in traction synchronous permanent magnet motors based on stator current spectrum

Introduction. The reliability of traction electric drive is a key factor determining the operational efficiency of urban electric transport. Bearing assemblies of electric motors remain one of the most common causes of failures leading to vehicle downtime.
Materials and methods. A method has been developed for on-board diagnostics of rolling bearing defects in a traction synchronous motor with permanent magnets (SDM) based on the analysis of stator current spectrum (MCSA) with equivalent three-phase current used to normalize diagnostic features. In the study simulation modeling in the MATLAB/Simulink environment was carried out, covering the range of defect angular dimension from 0° to 8° and speeds from 1500 to 8000 rpm.
Results. The method proposed sets clear limits to efficiency: the maximum speed for reliable defect detection is 5,000 rpm. At these and lower speeds in the stator current spectrum, a peak is observed of the mechanical rotor speed (fr), the amplitude of which correlates with the defect angular dimension. Normalization by equivalent operating current increases diagnostic accuracy.
Discussion and conclusions. The proposed method makes it possible to implement an on-board diagnostic system without installing additional sensors, which is especially important for urban electric buses operating mainly in the range of 2000-5000 rpm. A new method has been developed that integrates normalization by equivalent operating current to quantify the diagnostic sensitivity of the analysis of the stator current spectrum depending on the rotor rotation speed in a permanent magnet synchronous motor and determines the critical speed threshold of 5000 rpm.

1. Tavner PJ. Review of condition monitoring of rotating electrical machines. IET Electric Power Applications. 2008;2(4):215-247. DOI: 10.1049/iet-epa:20070280

2. Khan MA, Asad B, Kudelina K, Vaimann T, Kallaste A. The Bearing Faults Detection Methods for Electrical Machines–The State of the Art. Energies. 2023; 16(1): 296. DOI: 10.3390/en16010296

3. Santer P, Reinhard J, Schindler A, Graichen K. Detection of localized bearing faults in PMSMs by means of envelope analysis and wavelet packet transform using motor speed and current signals. Mechatronics. 2025; 106: 103294. DOI: 10.1016/j.mechatron-ics.2025.103294

4. Randall RB, Antoni J. Rolling element bearing diagnostics – a tutorial. Mechanical Systems and Signal Processing. 2011; 25(2): 485-520. DOI: 10.1016/j.ymssp.2010.07.017

5. He X, Liu X, Lin C, Fu M, Wang J, Zhang J. Early bearing fault diagnosis in PMSMs based on HOVMD and weighted evidence fusion of current–vibration signals. Sensors. 2025; 25(15): 4591. DOI: 10.3390/s25154591

6. Janrao S, Ambekar R. Fault diagnosis of an induction motor using motor current signature analysis. Int J Appl Eng Res. 2018;13(12):11002-11008.

7. Blödt M, Granjon P, Raison B, Rostaing G. Models for bearing damage detection in induction motors using stator current monitoring. IEEE Transactions on Industrial Electronics. 2008; 55(4): 1813-1822. DOI: 10.1109/TIE.2008.917108

8. Thomson WT, Gilmore RJ. Motor current signature analysis to detect faults in induction motor drives. Proceedings of the 32nd Turbomachinery Symposium. 2003: 145-156.

9. Ghorbel A, Eddai S, Limam B, Feki N, Haddar M. Bearing fault diagnosis based on artificial intelligence methods: machine learning and deep learning. Arab J Sci Eng. 2025; 50: 13605-13622. DOI: 10.1007/s13369-024-09488-3

10. Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D. Prognostics and health management design for rotary machinery systems. Mechanical Systems and Signal Processing. 2014; 42: 314-334. DOI: 10.1016/j.ymssp.2013.06.004

11. Kodkin VL, Anikin AS, Baldenkov AA. Spectral composition of induction motor rotor current as an indicator of its efficiency. Omsk Scientific Bulletin. 2019; (5): 39-45. DOI: 10.25206/1813-8225-2019-167-39-45 (In Russ.)

12. Zhileikin MM, Klimov AV, Maslennikov IK. Algorithm for generating accelerator pedal control signal providing energy-efficient power consumption of electric bus traction drive. Izvestiya MGTU «MAMI». 2022; 16(1): 51-60. DOI:10.17816/2074-0530-100232 (In Russ.)

13. Allal A, Khechekhouche A. Diagnosis of induction motor faults using the motor current normalized residual harmonic analysis method. International Journal of Electrical Power & Energy Systems. 2022; 141: 108219. DOI: 10.1016/j.ijepes.2022.108219

14. Kumar M, Deosarkar P, Inamdar S, Mahanty RN. Field oriented control of traction PMSM drive using WBG devices for EV application. In: Proceedings of the 2023 IEEE Renewable Energy and Sustainable E-Mobility Conference (RESEM); 2023; Bhopal, India. p. 1-8. DOI: 10.1109/RESEM57584.2023.10236344

15. Dvoeglazov EA, Zhileikin MM, Ospanbekov BK, Klimov AV. Diagnosis of material spalling on the bearing ring raceway of a permanent magnet synchronous electric motor. Gruzovik. 2023; (8): 25-30. DOI: 10.36652/1684-1298-2023-8-25-30 (In Russ.)

16. Ruiz-Sarrio JE, Antonino-Daviu JA, Martis C. Localized bearing fault analysis for different induction machine start-up modes via vibration time–frequency envelope spectrum. Sensors. 2024; 24(21): 6935. DOI: 10.3390/s24216935

17. Min TH, Lee JH, Choi BK. CNN-based fault classification in induction motors using feature vector images of symmetrical components. Electronics. 2025; 14(8): 1679. DOI: 10.3390/electronics14081679

18. Klimov AV. Investigation of methods for improving energy efficiency considering operating modes of traction electric drive using virtual mathematical modeling. Traktory i Selkhozmashiny. 2024; 91(1): 65-74. DOI: 10.17816/0321-4443-569208 (In Russ.)

19. Maksimov RO. Methodology of virtual bench testing for analysis of compatibility of vehicle system technical characteristics to detect and prevent resonance phenomena in the sprung cabin. Izvestiya MGTU «MAMI». 2023; 17(4): 387-400. DOI: 10.17816/2074-0530-456459 (In Russ.)

20. Qian D, Zhai X, Wei J. Modeling and Simulation of Complex Electromechanical System of More-Electric Aircraft Based on Distributed Simulation Technology. In: 2023 International Conference on Power Energy Systems and Applications (ICoPESA); 2023; Nanjing, China. Piscataway (NJ): IEEE; 2023. p. 868-873. DOI: 10.1109/ICoPESA56898.2023.10141103

21. Zhang B, Li H, Kong W, Ma J. Early-stage fault diagnosis of motor bearing based on kurtosis weighting and fusion of current–vibration signals. Sensors (Basel). 2024; 24(11): 3373. DOI: 10.3390/s24113373

22. Ye X, Li G. An intelligent fault diagnosis method for rolling bearing using motor stator current signals. Meas Sci Technol. 2024; 35(8): 086131. DOI: 10.1088/1361-6501/ad4bfc

23. Mohanty AR, Kar C. Monitoring gear vibrations through motor current signature analysis and wavelet transform. Mech Syst Signal Process. 2006; 20(1): 158-187. DOI: 10.1016/j.ymssp.2005.02.015

24. de las Morenas J, Belmonte LM, Morales R. Streamlined bearing fault detection using artificial intelligence in permanent magnet synchronous motors. Machines. 2025; 13(5): 357. DOI: 10.3390/ma-chines13050357

25. Belousov BN, Keller AV, Kharitonchik SV, Bakhmutov SV, Berdnikov AA. Problems of applied mechanics in the development of traction and transport vehicles with mechatronic modules. Avtomobilnaya Promyshlennost. 2020; (1): 8-16. (In Russ.)

26. Raouf I, Kumar P, Kim HS. Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network. Expert Syst Appl. 2024; 258: 125137. DOI: 10.1016/j.eswa.2024.125137

27. Li D, Wang W, Dong C, Fang T. Motor bearing fault diagnosis based on FFT-CNN-Transformer modeling. In: Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering; 2025 Jan 10-12; Hefei, China. p. 615-621. DOI: 10.1145/3727648.3727748

28. Kumar MP, Velpula S, Saiprakash C, et al. Advancements in fault detection and diagnosis methods for electric vehicles: a review. Discov Appl Sci. 2025; 7: 1235. DOI: 10.1007/s42452-025-07758-9

29. Zsuga A, Dineva A. Data-driven onboard inter-turn short circuit fault diagnosis for electric vehicles by using real-time simulation environment. IEEE Access. 2023; 11: 145447-145466. DOI: 10.1109/AC-CESS.2023.3344483

30. Keller AV, Popov AV. Methodology for synthesizing power distribution patterns between driving wheels of all-wheel drive agricultural vehicles. Traktory i Selkhozmashiny. 2023; 90(6): 505-514. DOI:10.17816/0321-4443-568209 (In Russ.)