Abstract
Induction machines serve as the cornerstone and driving force of modern manufacturing and production systems. This research aims to monitor and analyse the operating performance of induction motors using motor current signature analysis (MCSA). MCSA is employed to process the current signal of a motor into a frequency spectrum, known as the current signature by applying the Fast Fourier Transform (FFT) algorithm. The underlying principle is that vibration generated in a motor is closely related to the changes of the magnetic field density, and the induced voltage varies with the stator current.
A simulation model replicating the behaviour of an induction motor was developed and tested under various operating conditions. Advance techniques were used for data acquisition and analysis. The results obtained from both MATLAB/SIMULINK model and the experimental setup reveal that eccentricity faults in induction motors lead to increased current draw, resulting in transient instability, non-uniform acceleration and pulsating torque due to harmonic distortion.
Recommended Citation
Obianke, Francis Ikechukwu; Okhaifoh, Joseph; and Akinloye, Benjamin
(2026)
"CONDITION MONITORING OF INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS,"
Al-Bahir: Vol. 8:
Iss.
2, Article 8.
Available at: https://doi.org/10.55810/2313-0083.1131
References
[1] Sumit S Kahar, Devendra PI, Ankush IS, Subodh LN, Khan ZJ, Asutkar PG. Detection of ball bearing fault in induction motor using MCSA and harmonic analysis. World J Eng Res Technol 2020;6(4):144—58. ISSN 2454-695x.
[2] Idriss El-Thalji, Erkki Jantunen. A summary of 0888-3270/ fault modelling and predictive health monitoring of rolling element bearing. Mech Syst Signal Process 2015;60—61: 252—72. www.elsevier.com/locate/ymssphttps://doi.org/10. 1016/j.ymssp.2015.02/008c 2015 Elsevier Ltd.
[3] Konstantinos Mykoniatis. A real-time condition monitoring and maintenance management system for low voltage industrial motor using internet-of-things. In: Scientific committee of the international conference on industry 4.0 and smart manufacturing, 42; 2020. p. 450—42456. https://doi. org/10.1016/j.promfg.2020.02.050.
[4] Muhammad R, Jamal, Khaled S, Rasheed AI. Vibration measurement of a rotating shaft using electrostatic sensor. Int J Recent Technol Eng 2021;10(3). ISSN: 2277-3878 (online).
[5] Courrech J, Eshleman R,L. Condition monitoring of machinery. In: Piersol AG, Paez TL, editors. Harris's shock and vibration handbook. sixth ed. New York: McGraw-Hill; 2009. Ch. 16. ISBN 978-0-07-1508193.
[6] Li Yanlu, Baets Roel. In: Nonlinear signal errors in homodyne laser Doppler Vibrometry, 29. Optical Publishing Group; 2021. p. 8283—95. 2021, 6.
[7] Muhammad Irfan, Nordin Saad, Rosdiazli Ibrahim, Vijanth S Asirvandam, Muawia, Magzoub, Hung NT. A non-invasive method for condition monitoring of induction motor operating under arbitrary loading conditions. Research article — electrical Engineering. Arabian J Sci Eng 2025. https://doi.org/10.1007/s13369-015-1996-z.
[8] Rouaibia, Reda, Arbaoui, Fay Cal 720301and Bahi, Tahar. Fault eccentricity diagnosis in variable speed induction motor drive using DWT. Amse Journals-Amse IIETA Publication, 2017-Series Advances C Vol 72 No 3 pp 181-3202, https://doi.org/10.18280/ama-C.
[9] Priyanka M, Turk N, Dahiya R. Condition monitoring of induction motor through simulation of bearing fault and air gap eccentricity fault. Int J Recent Technol Eng 2019. ISSN: 2277-3878, vol-8 issue-3. Journal website, www.ijrte.org.
[10] Yassa N, Rachek M, Housassine H. Motor current signature analysis for the air gap eccentricity detection in the squirrel cage induction machines. Emerging and renewable Energy. Energy Proc 2019;162:251—62.
[11] Mohamad Hazwan, Mohd Ghazali, Wan Rahiman. Vibration analysis for machine monitoring and diagnosis. A systematic Review, University saints, Malaysia engineering campus. 2021. https://doi.org/10.1155/2021/9469318. Ibong Tebal 14300 Wan Rahiman; wanrahiman@usm.
[12] Olaleye OS, Christopher O, Ahiakwo Dikio, Idoniboyeobu C, Sunny Orike. Modeling of eccentricity and performance of three-phase induction motors. J New Views Eng Technol (JNET) 2020;2(1):97. Available online at: http:// www.rsujnet.org/index.php/publications/2020-edition.
[13] Yaabari N, Okoro OI, Akpama EJ. MATLAB-based simulation of a three-phase induction motor for dynamic studies. Niger J Technol 2022;41(6):1000—7. https://doi.org/10.4314/ njt.v41i6.10.
[14] Rahmatullah R, Serteller NFO, Topuz V. Modelling and Simulation of faulty induction motor in DQ reference frame using MATLAB/SIMULINK with MATLAB/GUIDE for Educational purpose. Int J Edu Inform Technol 2023;17. https://doi.org/10.46300/9109.2023. 17,2.
[15] Kidd B. Vector-based magnetic circuit modelling of induction motors. Faculty of information technology, Monash university. Exhibition walk, Clayton, Vic Australia. Magnetism2; 2002. p. 130—51. https:/doi.org/10.3390/magnetism.
[16] Eyup, Irgat, Unsal, A., Huseyin, T. C. Detection of eccentricity faults of induction motor based on decision Trees. Ulakbim Usal- Dumlupinar University, Kutahya. Turkey. Downloaded on Jan. 22, 2022 at 21:04:14 UTC from IEEE Xplore.
[17] Rene Jaros, Radek B, Jakub D, Lukas D, Jan B, Juz K, Perk Z, Radek M. Advanced signal processing methods for condition monitoring. Arch Comput Method Eng 2023;30: 1533—77. https://doi.org/10.1007/S11831-022-09834-4.
[18] Michele Sintoni, Macrelli Elena, Bellini Alberto, Bianchini Claudio. Condition monitoring of induction machines; quantitative analysis and comparison. MDPI Sensors 2023;23:1046. https://doi.org/10.3390/s23021046.
[19] Roberto Diversi, Lenzi Alice, Speciale Nicolo, Barbieri Matteo. An autoregressive-based motor current signature analysis approach for fault diagnosis of electric motor-driven mechanisms. MDPI Sensors 2025;25:1130. https://doi.org/10.3390/s25041130.
[20] Manjeevan, Seera, Chee, Peng, Lim, Saeid, Nahavandi and Chu, Kiong, Loo. Condition monitoring of induction motors; A review and application of an ensemble of hybrid intelligent models; Published 2014 (Elsevier journal), reposted June 6, 2024 (Figshare Version 2) Journal, Expert system with applications, Vo. 41, Issue10, Pages 4891- 4903.
[21] Rahmatullah M, Kumar R, Singh D. Modeling and simulation of faulty induction motors in DQ reference frame for fault diagnosis applications. IEEE Access 2023;11:45632—44. https://doi.org/10.1109/ACCESS.2023.3261122.
[22] Yadav P, Sigh SP. Experimental validation of induction motor fault detection under healthy and faulty conditions. J Elect Eng Technol 2022;17(6):3037—49. https://doi.org/10. 1007/s42835-022-01155-7.
[23] Niazi MS, Khan A, Ullah R. Minor eccentricity fault diagnosis in induction motors using motor current signature analysis and machine learning techniques. Int J Electr Power Energy Syst 2024;158:109993. https://doi.org/10.1016/ j.ijepes.2024.109993.
[24] Adefarati T, Saha TK. Adaptive fault modeling for condition monitoring of induction motors under nonlinear loading. IEEE Trans Ind Electron 2023;70(5):5124—35. https://doi.org/ 10.1109/TIE.2023.3234567.
[25] Li X, Zhang H, Chem Y. Detection of critical eccentricity faults in induction motors using torque harmonic analysis and vibration signatures. Mech Sys Signal Proc 2023;188: 110004. https://doi.org/10.1016/j.ymssp.2023.110004.
[26] Zhou L, Wang J, Patel R. IoT-enabled hybrid condition monitoring of induction motors using vibration and current signals. IEEE Internet Things J 2025;12(3):2456—568. https:// doi.org/10.1109/JIOT.2025.3345678.
Included in
Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Electronic Devices and Semiconductor Manufacturing Commons





Indexed in: