FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-140222

Research Article |

A Diagnostic Method for Induction Motor Inter-Turn Faults Based on Wavelet Transform and Neural Networks

Author(s): Diwakar Verma1,2*, Dr. Ambarisha Mishra3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 14, Issue 2

Publisher : FOREX Publication

Published : 25 June 2026

e-ISSN : 2347-470X

Page(s) : 458-466

DOI: https://doi.org/10.37391/IJEER.140222

Abstract

Conventional protection systems for induction motors often fail to detect inter-turn short circuit (ITSC) faults accurately. These faults are usually misclassified as overload or phase imbalance, which may lead to unnecessary tripping and downtime. Early detection of ITSC faults is important, as fault severity increases rapidly due to insulation degradation. This paper presents a diagnostic method based on wavelet transform and artificial neural networks (WTANN). The method uses stator current signals for fault detection without requiring additional sensors. The extracted features are used to classify motor conditions as ITSC, overload (OL), or short circuit fault (SCF). Experimental results on a 1 Hp, 415 V induction motor show that the proposed method achieves 97.4% testing accuracy, compared to 24.44% for the FFT-based method. The method performs well even under noisy conditions (20 dB and 30 dB). The results show that the proposed approach is accurate and suitable for practical applications.

Keywords: Induction Motor, Protection, Inter-Turn Fault, Wavelet Transform, ANN.




Diwakar Verma, Research Scholar, Department of Electrical Engineering, NIT Patna, India.; Email: diwakarvermasonu@gmail.com

Diwakar Verma, Assistant Professor, EE Department, Bhagalpur College of Engineering, Bhagalpur; Email: diwakarvermasonu@gmail.com

Dr. Ambarisha Mishra, Assistant Professor, EE department, NIT Patna, Bihar, India; Email: ambrish.mishra@nitp.ac.in

    [1] Athikessavan Subash Chandar, Elango Jeyasankar, Panda Sanjib Kumar. Inter-turn fault detection of induction motors using end-shield leakage fluxes. IEEE Trans Energy Convers 2022. http://dx.doi.org/10.1109/TEC.2022.3174891.
    [2] Reed David M, Hofmann Heath F, Sun Jing. Offline identification of induction machine parameters with core loss estimation using the stator current locus. IEEE Trans Energy Convers 2016;31(4):1549–58.
    [3] Eftekhari Maryam, Moallem Mehdi, Sadri Saeed, Hsieh Min-Fu. Online detection of induction motor’s stator winding short-circuit faults. IEEE Syst J 2013;8(4):1272–82.
    [4] Singh Girish Kumar, Ahmed Saleh Al Kazzaz Sa’ad. Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine. Electr Power Syst Res 2004;68(2):119–36.
    [5] Patel Rakesh A, Bhalja Bhavesh R. Condition monitoring and fault diagnosis of induction motor using support vector machine. Electr Power Compon Syst 2016;44(6):683–92.
    [6] Ali Mohammad Zawad, Shabbir MdNasmus Sakib Khan, Liang Xiaodong, Zhang Yu, Hu Ting. Machine learning-based fault diagnosis for single-and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Trans Ind Appl 2019;55(3):2378–91.
    [7] Ballal Makarand S, Khan Zafar J, Suryawanshi Hiralal M, Sonolikar Ram L. Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Trans Ind Electron 2007;54(1):250–8.
    [8] Palácios Rodrigo H Cunha, Silva Ivan Nunes Da, Goedtel Alessandro, Godoy Wagner F. A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electr Power Syst Res 2015;127:249–58.
    [9] Bazan Gustavo Henrique, Scalassara Paulo Rogério, Endo Wagner, Goedtel Alessandro, Godoy Wagner Fontes, Palácios Rodrigo Henrique Cunha. Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electr Power Syst Res 2017;143:347–56.
    [10] Husari Fatimatelbatoul, Seshadrinath Jeevanand. Early stator fault detection and condition identification in induction motor using novel deep network. IEEE Trans Artif Intell 2021. http://dx.doi.org/10.1109/TAI.2021.3135799.
    [11] Mejia-Barron Arturo, Tapia-Tinoco Guillermo, Razo-Hernandez Jose R, Valtierra-Rodriguez Martin, Granados-Lieberman David. A neural network-based model for MCSA of inter-turn short-circuit faults in induction motors and its power hardware in the loop simulation. Comput Electr Eng 2021;93. http://dx.doi.org/10.1016/j.compeleceng.2021.107234.
    [12] Sarkar Shubhasish, Purkait Prithwiraj, Das Santanu. NI Compact RIO-based methodology for online detection of stator winding inter-turn insulation faults in 3-phase induction motors. Measurement 2021;182. http://dx.doi.org/10.1016/j.measurement.2021.109682.
    [13] Gangsar Purushottam, Tiwari Rajiv. Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mech Syst Signal Process 2020;144:106908.
    [14] Surya Gulamfaruk N, Khan Zafar J, Ballal Makarand S, Suryawanshi Hiralal M. A simplified frequency-domain detection of stator turn fault in squirrel-cage induction motors using an observer coil technique. IEEE Trans Ind Electron 2016;64(2):1495–506.
    [15] Mirzaeva Galina, Saad Khalid Imtiaz. Advanced diagnosis of stator turn-to-turn faults and static eccentricity in induction motors based on internal flux measurement. IEEE Trans Ind Appl 2018;54(4):3961–70.
    [16] Ramirez-Nunez Juan A, Antonino-Daviu Jose A, Climente-Alarcón Vicente, Quijano-López Alfredo, Razik Hubert, OsornioRios Roque A, Romero-Troncoso Rene de J. Evaluation of the detectability of electromechanical faults in induction motors via transient analysis of the stray flux. IEEE Trans Ind Appl 2018;54(5):4324–32.
    [17] Gyftakis Konstantinos N, Marques Cardoso Antonio J. Reliable detection of stator interturn faults of very low severity level in induction motors. IEEE Trans Ind Electron 2020;68(4):3475–84.
    [18] Kumar Padmanabhan Sampath, Xie Lihua, Halick Mohamed Sathik Mohamed, Vaiyapuri Viswanathan. Stator end-winding thermal and magnetic sensor arrays for online stator inter-turn fault detection. IEEE Sens J 2020;21(4):5312–21.
    [19] Dongare Ujwala Vikas, Umre Bhimrao Sitaram, Ballal Makarand Sudhakar. Rotor winding inter-turn short-circuit fault detection in wound rotor induction motors using Wing Technique. J Power Electron 2022;22(4):614–28.
    [20] P. C. Krause, O. Wasynczuk, and S. D. Sudhoff, Analysis of Electric Machinery and Drive Systems, 2nd ed., IEEE Press, 2002.
    [21] Kamble, S.P., Thawkar, S., Gaikwad, V.G. et al. A Comparative Analysis for Selection of Appropriate Mother Wavelet for Detection of Stationary Disturbances. J. Inst. Eng. India Ser. B 98, 533–540 (2017).
    [22] Kamble, S., Chaturvedi, P., Chen, CJ. et al. Enhancing reliability and efficiency of grid-connected solid-state transformer through fault detection and classification using wavelet transform and artificial neural network. Electr Eng 106, 2525–2535 (2024). https://doi.org/10.1007/s00202-023-02080-2.
    [23] Ratna, S., Muflih, M., Budiman, H., Syapotro, U., & Hamdani, M. (2024). Comparison of ELM, LSTM, and CNN Models in Breast Cancer Classification. Journal of Data Science, 2024.
    [24] Adnan Dehghani, Hamza Mohammad Zakir Hiyat Moazam, Fatemehsadat Mortazavizadeh, Vahid Ranjbar, Majid Mirzaei, Saber Mortezavi, Jing Lin Ng, Amin Dehghani, Comparative evaluation of LSTM, CNN, and ConvLSTM for hourly short-term streamflow forecasting using deep learning approaches, Ecological Informatics, Volume 75, 2023, 102119.

Diwakar Verma, and Dr. Ambarisha Mishra (2026), A Diagnostic Method for Induction Motor Inter-Turn Faults Based on Wavelet Transform and Neural Networks. IJEER 14(2), 458-466. DOI: 10.37391/IJEER.140222.