Fault Detection in Three-phase Induction Motor based on Data Acquisition and ANN based Data Processing

  • Ovidiu Gheorghe Moldovan Department of Mechatronics, University of Oradea, Romania
  • Remus Vladimir Ghincu Doctoral School of Industrial Engineering, University of Oradea, Romania
  • Alin Octavian Moldovan Doctoral School of Industrial Engineering, University of Oradea, Romania
  • Dan Noje Doctoral School of Industrial Engineering, University of Oradea, Romania
  • Radu Catalin Tarca Department of Mechatronics, University of Oradea, Romania


The main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks.


[1] O. Abdeljaber, O. Avci, S. Kiranyaz, M. Gabbouj, and D. J. Inman (2017). Real-time vibrationbased structural damage detection using one-dimensional convolutional neural networks Journal of Sound and Vibration, ol. 388, pp. 154-170, Feb. 2017.

[2] Agoston, K.(2015). Fault Detection of the Electrical Motors Based on Vibration Analysis. Procedia Technology, vol. 19, pp.547-553.

[3] Bao, X., Meng, X., Fu, H., 2014 (2014). A Study of Financial Distress Prediction based on Discernibility Matrix and ANN PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND MANAGEMENT INNOVATION , pp. 361-365.

[4] Cristalli, C. and Paone, N. and Rodríguez, R.M.(2006). Mechanical fault detection of electric motors by laser vibrometer and accelerometer measurements Mechanical Systems and Signal Processing, vol. 20, pp. 1350-1361.

[5] Dempsey P. J., Kreider G. , and Fichter T. (2006). Investigation of Tapered Roller Bearing Damage Detection Using Oil Debris Analysis IEEE Aerospace Conference, Big Sky, MT, USA, 2006, pp. 1-11.

[6] Eski, I., Erkaya, S., Savas, S., & Yildirim, S. (2011) Fault detection on robot manipulators using artificial neural networks. Robotics and Computer-Integrated Manufacturing , 27(1), 115-123.

[7] Glowacz, A.,Glowacz, W., Glowacz, Z. (2018). Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals Measurement, 113, 1-9, https://linkinghub.elsevier.com/retrieve/pii/S0263224117305432

[8] Guhmann, C., Filbert, D., (1992). Fault-diagnosis of electric low-power motors by analyzing the current signal IFAC SYMPOSIA SERIES, 113, 141-146

[9] Kadri, A., Mohammadi, F.,(2020). ANN Daily Peak Forecast for Peak Demand Charges Management 2020 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE).,

[10] Kishore, PVV; Prajwal, KS; Mohan, MK; Koteswarao, S;AF Kishore, P. V. V.; Prajwal, K. Sai; Mohan, M. Kamal; Koteswarao, S. (2015). Medical Image Watermarking with ANN in Wavelet Domain 2015 IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTING AND COMMUNICATION TECHNOLOGIES (CONECCT), JUL 10-11, 2015, Bangalore, INDIA Mathworks®. MATLAB, Neural Network Toolbox, Image Processing Toolbox, R2016b, User's Guide. Available online: https://www.mathworks.com/help/ (accessed on 4 November 2017).

[11] Mathworks®. MATLAB, Neural Network Toolbox, Image Processing Toolbox, R2019b, User's Guide Available online: https://www.mathworks.com/help/ (accessed on 31 March 2022).,

[12] Maruthi G. S. and Hedge V.(2016). Application of MEMS accelerometer for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor IEEE Sensors Journal, vol. 16, no. 1, pp. 145-152, 2016.

[13] Malekian A. , Chitsaz N , (2021). Chapter 4 - Concepts, procedures, and applications of artificial neural network models in streamflow forecasting, Advances in Streamflow Forecasting, Elsevier,Editor(s): Priyanka Sharma, Deepesh Machiwal , 2021,Pages 115-147,ISBN 9780128206737.

[14] Montt, C.; Castro, J.C. ; Valencia, A.; Oddershede, A.; Quezada, L. (2020). Artificial Neural Network and Nonlinear Regression Model for Predicting Electrical Pole Crash International Journal of Computers Communications & Control, 15(5), 3879, 2020.

[15] Majerova, D., Kukal, J., (2005). Lukasiewicz ANN for local image processing. NEURAL NETWORK WORLD , 15, 535-551.

[16] Junior R. F. R., Areias I. A. dos S., Campos M. M., Teixeira C. E. , da Silva L. E. B. , and Gomes G. F. (2022). Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals Measurement, vol. 190, p. 110759, Feb. 2022.

[17] Han, S., Zhang, S., Li, Y. and Chen, L. (2021). The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit International Journal of Intelligent Computing and Cybernetics , Vol. ahead-of-print No. ahead-of-print.

[18] Li L., Mechefske C. K., and Li W.(2004). Electric motor faults diagnosis using artificial neural networks Insight - Non-Destructive Testing and Condition Monitoring , vol. 46, no. 10, pp. 616-621, Oct. 2004.

[19] T. G. Luis Alonso, M. Johnny Rodriguez, M. A. Moonem, and M. A. Platas-Garza(2018). A multiresolution Taylor-Kalman approach for broken rotor bar detection in cage induction motors IEEE Transactions on Instrumentation and Measurements, vol. 67, no. 6, pp. 1317-1328, 2018.

[20] Lozano-Garzon, C.; Montoya, G. A.; Donoso, Y. (2020). A Green Routing Mathematical Model for IoT Networks in Critical Energy Environments International Journal of Computers Communications & Control , 15(4), 3914, 2020.

[21] Principi E., Rossetti D. , Squartini S. , and Piazza F. (2019). Unsupervised electric motor fault detection by using deep autoencoders IEEE/CAA J.Autom. Sinica, , vol. 6, no. 2, pp. 441-451, Mar. 2019.

[22] Rajamany, G.; Srinivasan, S.; Rajamany, K.; Natarajan, R.K. (2006). Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network. Journal of Electrical and Computer Engineering 2019, 2019, 1-11.

[23] Rajagopalan, T. G. Habetler, R. G. Harley, T. Sebastian, and B. Lequesne(2006). Current/Voltage-Based Detection of Faults in Gears Coupled to Electric Motors IEEE Trans. on Ind. Applicat., vol. 42, no. 6, pp. 1412-1420, Nov. 2006.

[24] Rosle, N.; Fadzail, N.F.; Halim, M.I.A.; Rohani, M.N.K.H.; Fahmi, M.I.; Leow, W.Z.; Bakar, N.N.A. (2020). Fault Detection and Classification in Three Phase Series Compensated Transmission Line Using ANN J. Phys.: Conf. Ser., 2020, 1432, 012013.

[25] Refaat S. , Abu-Rub H., Saad M. S. , Aboul-Zahab E. M., and Iqbal A. (2006). Discrimination of stator winding turn fault and unbalanced supply voltage in permanent magnet synchronous motor using ANN 4th International Conference on Power Engineering, Energy and Electrical Drives, Istanbul, Turkey, May 2013, pp. 858-863.

[26] Ribeiro Junior, R.F., Areias, I.A.d.S. and Gomes, G.F. (2021) Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types Sensor Review, Vol. 41 No. 3, pp. 311-319.

[27] Santos H. , Scalassara P. ,Endo W. ,Goedtel A. ,Guedes J. , and Gentil M. ,(2021) Non-invasive sound-based classifier of bearing faults in electric induction motors International Journal of Pattern Recognition and Artificial Intelligence, IET sci. meas. technol., vol. 15, no. 5, pp. 434-445, Jul. 2021.

[28] Senthil Kumar, R., Gerald Christopher Raj, I., Suresh, K.P., Leninpugalhanthi, P., Suresh, M., Panchal, H., Meenakumari, R., Sadasivuni, K.K.,(2021) A method for broken bar fault diagnosis in three phase induction motor drive system using Artificial Neural Networks International Journal of Ambient Energy, 1-7.

[29] Sharma, Amandeep and Mathew, Lini and Chatterji, Shantanu and Goyal, Deepam(2020) Artificial Intelligence-Based Fault Diagnosis for Condition Monitoring of Electric Motors International Journal of Pattern Recognition and Artificial Intelligence, vol. 34, Issue 6, pp.9429-9441, 2020.

[30] Shifat, Tanvir Alam and Hur, Jang-Wook (2021) ANN Assisted Multi Sensor Information Fusion for BLDC Motor Fault Diagnosis IEEE Access, vol. 9, pp.9429-9441, 2021.

[31] Sunisa, S., Jittiwut, S.(2011) Detection of a motor bearing shield fault using neural networks SICE Annual Conference 2011, IEEE, Tokyo, Japan.

[32] Sun, S.; Hu, B.; Yu, Z.; Song, X.N. (2020). A Stochastic Max Pooling Strategy for Convolutional Neural Network Trained by Noisy Samples International Journal of Computers Communications & Control, 15(1), 1007, 2020.

[33] Taplak, H., Uzmay, I., & Yıldırım, S. (2006). An artificial neural network application to fault detection of a rotor bearing system. Industrial Lubrication and Tribology , 58(1), pp.32-44.

[34] Tian, Y. H.; Wu, Q.; Zhang, Y. (2020) A Convolutional Long Short-Term Memory Neural Network Based Prediction Model International Journal of Computers Communications & Control, 15(5), 3906, 2020.

[35] Walczak, S.; Cerpa, N. (2003). Artificial Neural Networks. In Encyclopedia of Physical Science and Technology, Elsevier, 2003; pp. 631-645 ISBN 978-0-12-227410-7.

[36] WangH. ,Xu J. ,Yan R. , and Gao R. X. (2019) A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN IEEE Trans. Instrum. Meas., vol. 69, no. 5, pp. 2377-2389, May 2020.

[37] Xie Y. and Wang Y. (2014) 3D temperature field analysis of the induction motors with broken bar fault Applied Thermal Engineering, vol. 66, no. 1-2, pp. 25-34, May 2014.

[38] Xhako, D; Hyka, N (2019) Medical Image Prediction Using Artificial Neural Networks TURKISH PHYSICAL SOCIETY 35TH INTERNATIONAL PHYSICS CONGRESS (TPS35), Volume2178, Article Number030053, Published 2019

[39] Yang, Z.; Kong, C.; Wang, Y.; Rong, X.; Wei, L.(2021) Fault Diagnosis of Mine Asynchronous Motor Based on MEEMD Energy Entropy and ANN Computers & Electrical Engineering, 2021, 92, 107070.

[40] Yildirim, S., & Eski, I. (2010). Noise analysis of robot manipulator using neural networks Robotics and Computer-Integrated Manufacturing , 26(4), 282-290

[41] Yilmaz M. S. and Ayaz E. (2009) Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data IEEE EUROCON 2009, St. Petersburg, Russia, May 2009, pp. 1140-1145.

[42] Zhang, Z.-Y., Wang, K.-S., (2014) Wind turbine fault detection based on SCADA data analysis using ANN. Adv. Manuf. , 70-78.

[43] Zhao, F. (2020) A Neural Network Classification Model Based on Covering Algorithm and Immune Clustering Algorithm International Journal of Computers Communications & Control, 15(1), 1008, 2020.
How to Cite
MOLDOVAN, Ovidiu Gheorghe et al. Fault Detection in Three-phase Induction Motor based on Data Acquisition and ANN based Data Processing. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 17, n. 3, apr. 2022. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/4788>. Date accessed: 22 may 2022. doi: https://doi.org/10.15837/ijccc.2022.3.4788.