AN ENHANCED STRAY FLUX APPROACH FOR ROTOR BAR FAULT DETECTION IN INDUCTION MACHINES
DOI:
https://doi.org/10.59277/RRST-EE.2026.3.3Keywords:
Broken bars, Diagnostic, Finite element analysis, Induction motors, Modeling, Monitoring, Signal processingAbstract
This paper presents an enhanced method for detecting broken rotor bar (BRB) faults in squirrel-cage induction motors (SCIMs), with a focus on low- and no-load conditions. The approach relies on harmonic analysis of stray magnetic flux and is benchmarked against conventional Motor Current Signature Analysis (MCSA). Fault scenarios, including single and multiple broken bars in adjacent and non-adjacent configurations, are investigated using finite element simulations of a 2.2 kW motor. Results show that stray flux indicators provide higher sensitivity and accuracy than stator current–based methods. Experimental validation under varying load conditions (slip 0.049–0.092) confirms the effectiveness of the proposed approach, particularly under no-load operation. These findings highlight its suitability for real-time condition monitoring and predictive maintenance of SCIMs.
References
1. A. G. Yetgin and M. M. Tezcan, “Effect of the Lambda Parameter on Three-Phase Induction Motor Design by Analytical and Magnetic Methods,” Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., Vol. 70, No. 4, pp. 435–440, Nov. 2025, doi: 10.59277/rrst-ee.2025.4.1.
2. E.-G. Boudissa, M. Bounekhla, and F. Habbi. “Enhanced Spiral Dynamic Algorithm with Application to Induction Motor Parameters Identification”, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., Vol. 70, No. 4, pp. 441–446, Nov. 2025, doi:10.59277/rrst-ee.2025.4.2
3. J. Li, C. Zheng, Z. Wu, H. Chen, X.-B. Wang, and Z.-X. Yang, “Review of fault diagnosis for rotating machinery: Prior knowledge integration in data-driven methods benefits model interpretability and generalizability,” Mechanical Systems and Signal Processing, Vol. 242, pp. 113623, Jan. 2026, doi: 10.1016/j.ymssp.2025.113623.
4. R. R. Kumar, L. O. Waisale, J. L. Tamata, A. Tortella, S. H. Kia, and M. Andriollo, “Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques,” Machines, Vol. 12, No. 12, pp. 890, Dec. 2024, doi: 10.3390/machines12120890.
5. S. Reza Mortezaei, M. Hosseini Aliabadi, and S. Javadi, “Eccentricity fault detection in surface-mounted permanent magnet synchronous motors by analytical prediction, FEM evaluation, and experimental magnetic sensing of the stray flux density,” Journal of Magnetism and Magnetic Materials, Vol. 589, pp. 171441, Jan. 2024, doi: 10.1016/j.jmmm.2023.171441.
6. N. A. R. Maciejewski, R. Z. Freire, A. L. Szejka, T. P. M. Bazzo, V. B. Frencl, and A. E. Treml, “Comparative analysis of feature selection and classification techniques for robust broken rotor bar diagnosis in induction motors using current and vibration signals”, Auton. Intell. Syst., Vol. 5, No. 1, Oct. 2025, doi: 10.1007/s43684-025-00113-0.
7. M. B. Shahid et al., “Model predictive control for energy efficient AC motor drives: An overview”, IET Electric Power Appl, Vol. 18, No. 12, pp. 1894–1920, Nov. 2024, doi: 10.1049/elp2.12517.
8. R. P. P. de Souza, C. M. Agulhari, A. Goedtel, D. Moriñigo-Sotelo, Ó. Duque-Pérez, and V. Fernández-Cavero, “Broken rotor bar fault detection — A virtual current approach combined with a robust linear parameter-varying observer,” Control Engineering Practice, Vol. 156, pp. 106233, Mar. 2025, doi: 10.1016/j.conengprac.2024.106233.
9. P. Pohakar, R. Gandhi, S. Hans, G. Sharma, and P. N. Bokoro, “Analysis of multiple faults in induction motor using machine learning techniques”, e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol. 12, p. 101007, Jun. 2025, doi: 10.1016/j.prime.2025.101007.
10. R. R. Kumar, L. O. Waisale, J. L. Tamata, A. Tortella, S. H. Kia, and M. Andriollo, “Advanced Fault Detection and Severity Analysis of Broken Rotor Bars in Induction Motors: Comparative Classification and Feature Study Using Dimensionality Reduction Techniques,” Machines, Vol. 12, No. 12, p. 890, Dec. 2024, doi: 10.3390/machines12120890.
11. Y. Bendjeddou, A. Seghiour, M. Taib, H. Sellamna, A. Tertag, And N. Benouzza, “Design Sensitivity Analysis And Performance Improvement Of A Six-Phase Squirrel-Cage Induction Machine”, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., Vol. 71, No. 1, pp. 3–8, Mar. 2026, doi: 10.59277/rrst-ee.2026.1.1.
12. A. Mabrek, Z. Elyazid, B. Selmoune, and K. E. Hemsas, “Modeling broken rotor bar faults in induction motors: a combined SSFR and FEM approach”, Bulletin EEI, Vol. 14, No. 6, pp. 4356–4367, Dec. 2025, doi: 10.11591/eei.v14i6.10455.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
