Magnetic rotor breakage study in permanent magnet synchronous motor at COMSOL multiphysics and fault detection using machine learning


  • Said Benkaihoul
  • Lakhdar Mazouz
  • Toufik Tayeb Naas
  • Özüpak Yıldırım
  • Amar Regaz



magnetic rotor breakage (MRB), machine learning (ML), fault detection, COMSOL multiphysics


Electric vehicles are one of the most important means in the industrial sector due to their frequent use and depend primarily on electric motors. Electric motors of all types, synchronous and asynchronous, face many faults in the rotor and stator, affecting the performance's reliability. Researchers are seeking to find ways that enable us to detect and diagnose faults in electric motors based on smart and fast methods. Early detection of problems in electric motors is vital, especially in areas such as electric vehicles. This study focuses on magnetic rotor breakage (MRB) in permanent magnet synchronous motors (PMSM). We use a simulation tool such as COMSOL Multiphysics as a simulation tool. This platform is a widely used software for modeling and analyzing complex electromagnetic systems. The study also addresses fault detection using machine learning. This involves using data analysis and pattern recognition techniques to distinguish between normal and defective states of the motor. This is an important step to improve the reliability of motors and identify potential failures in advance. Five different machine learning algorithms such as Extreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB), Naive Bayes (NB), and Random Forest (RF) are used in the study. Data from four different cases obtained from the PMSM design were used to train and test the machine-learning models. The results obtained show how accurate the proposed models are in diagnosing PMSM problems, especially MRB.


REGAZ, A. et al. Composites Active Material for Detection of Faults in the Asynchronous Motor. n. 1, August, 2022.

REGAZ, A. et al. Detection of Broken Rotor Bars ( BRB ) in the asynchronous machine by the use of smart materials. n. 1,August, 2018.

KUMAR, P.; HATI, A. S. Review on Machine Learning Algorithm Based Fault Detection in Induction Motors. Archives of Computational Methods in Engineering, v. 28, n. 3, p. 1929–1940, 2021.

QUABECK, S. et al. Detection of Broken Rotor Bars in Induction Machines using Machine Learning Methods. 23rd International Conference on Electrical Machines and Systems, ICEMS 2020, p. 620–625, 2020.

ZHANG, X.; ZHAO, B.; LIN, Y. U. N. Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data : A Review. IEEE Access, v. PP, p. 1, 2021.

THAMKE, P. W. et al. Faults Associated With Permanent Magnet Synchronous Motor. International Journal Of Core Engineering & Management (IJCEM, v. 2, n. 3, p. 9510, 2015.


SUBHA LAKSHMI, N.; ALLIRANI, S. Modelling and Simulation of Permanent Magnet Synchronous Motor for Performance Enhancement Using ANSYS Maxwell.Springer Singapore, 2021.

ZHANG, G.; LI, K.; LIU, C. Simulation of Permanent Magnet Synchronous Motor Vector Control System Based on Simplorer a Maxwell. v. 163, n. Iceesd, p. 1977–1982, 2018.

MERSHA, T. K.; DU, C. Co-simulation and modeling of PMSM based on ansys software and simulink for EVs. World Electric Vehicle Journal, v. 13, n. 1, p. 1–12, 2022.

KASSA, M. T.; CHANGQING, D. Design Optimazation and Simulation of PMSM based on Maxwell and TwinBuilder for EVs. 2021 8th International Conference on Electrical and Electronics Engineering, ICEEE 2021, p. 99–103, 2021.

WU, J. et al. Efficiency Optimization of PMSM Drives Using Field-Circuit Coupled FEM for EV/HEV Applications. IEEE Access, v. 6, p. 15192–15201, 2018.

NIAN, F.; YU, Y. PMSM Demagnetization Fault Diagnosis Based on Back-EMF Signal. Journal of Physics: Conference Series, v. 2395, n. 1, 2022.

KUMAR, P.; BALAKRISHNAN, P. EWT Implementation for Examining Demagnetization Fault in PMSM using FEM. 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES). Anais...IEEE, 2022.

GAYATRI, M. V. N. A. S.; PRAVEEN KUMAR, N. Application of Machine Learning for Analyzing Demagnetization Fault in IPMSM using Finite Element Method. 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022, p. 1–5, 2022.

KRICHEN, M. et al. Effects of Airgap Static Eccentricity in Permanent Magnet Synchronous Motors by Means of Finite Element Analysis. Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, p. 422–427, 2020.

LIN, F.; ZUO, S.; DENG, W. Impact of rotor eccentricity on electromagnetic vibration and noise of permanent magnet synchronous motor. Journal of Vibroengineering, v. 20, n. 2, p. 923–935, 2018.

AGGARWAL, A.; STRANGAS, E. G. Review of detection methods of static eccentricity for interior permanent magnet synchronous machine. Energies, v. 12, n. 21, 2019.

GHOSH, S. Method for Fault Diagnosis and Speed Control of PMSM. Computer Systems Science and Engineering, v. 45, n. 3, p. 2392–2404, 2023.

MEZNI, Z.; DELPHA, C.; DIALLO, D. Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features. p. 1–18, 2022.

SUJATHA, C.; MOHAN, A. Bearing Fault Classification Using Multi-ClassMachine Learning ( ML ) Techniques. N. 1. Ml, p. 1–10,2024.

RUAN, D. et al. Advanced Engineering Informatics CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis . Advanced Engineering Informatics, v. 55, n. October 2022, p. 101877, 2023.

MINE, Coal et al. Fault Diagnosis of Permanent Magnet Synchronous Motor of. 2022.

MUELLER, P. N.; WOELFL, L.; CAN, S. Engineering Applications of Artificial Intelligence Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement. Engineering Applications of Artificial Intelligence, v. 126, n. PA, p. 106834, 2023.

VGG, L. Detection and Identification of Demagnetization and. 2020.

Li, Fangli, et al. Fault diagnosis of permanent magnet synchronous motor inter turn short circuit based on deep reinforcement learning. Journal of Physics: Conference Series. v. 2137. n. 1. IOP Publishing, 2021.

FANG, Y.; WANG, M.; WEI, L. Deep Transfer Learning in Inter-turn Short Circuit Fault Diagnosis of PMSM. 2021 IEEE International Conference on Mechatronics and Automation (ICMA). Anais...IEEE, 2021.

BENSALEM, Y.; ABDELKRIM, M. N. Modeling and simulation of induction motor based on finite element analysis. International Journal of Power Electronics and Drive Systems, v. 7, n. 4, 2016.

STEFANO, F. S.; ADEMIR, N. FEM Applied to Evaluation of the Influence of Electric Field on Design of the Stator Slots in PMSM. IEEE Latin America Transactions, v. 17, n. 4, p. 590–596, 2019.

HASSAN, M. U.; NILSSEN, R.; RØKKE, A. Analysis of electromagnetic behavior of Permanent Magnetized ( PM ) electrical machines in fault modes. n. 1, August, p. 2–3, 2017.

IRGAT, E.; UNSAL, A. Comparative Evaluation of Machine Learning Methods for the Detection of the Eccentricity Faults of Induction Motors by Using Vibration Signals. p. 1–19, 2022.



How to Cite

Benkaihoul, S., Mazouz, L., Naas, T. T., Yıldırım, Özüpak, & Regaz, A. (2024). Magnetic rotor breakage study in permanent magnet synchronous motor at COMSOL multiphysics and fault detection using machine learning. STUDIES IN ENGINEERING AND EXACT SCIENCES, 5(1), 603–618.