Neural Classification of Rotor Faults in Three-Phase Induction Motors using Electric Current Signals in the Frequency Domain
Keywords:Motor Faults, FFT, Multilayer Perceptron, Artificial Neural Network
Three-phase induction motors are widely used in different applications in the industry due to their robustness, low cost, and reliability. Untimely identification and correct diagnosis of incipient faults reduce cost and improve the maintenance management of these machines. This paper explores a new method for robust classification of rotor failures in three-phase induction motors (MITs) connected directly to the electrical network, operating in a steady-state, under unbalanced voltages and load conditions. Through an innovative methodology, an analysis of the electrical current signals from 1 hp and 2 hp motors in the frequency domain was performed. Such analysis was applied in constructing input matrices for a Multilayer Perceptron Neural Network (MLPNN) to detect faults. Furthermore, this methodology proved to be robust because the samples of the failing and healthy motors include voltage unbalance conditions in the electrical supply and a significant variation in the load applied to the motor shaft. Such load variation was used for the detection of failures of 1, 2, and 4 broken bars consecutively on the rotor and in the condition of 2 broken bars and 2 other broken bars diametrically opposite. The results were promising and were obtained using 847 real samples from an experimental bench used to construct the neural model and its respective validation.
BAZAN, G. H. et al. Stator short-circuit diagnosis in induction motors using mutual information and intelligent systems. IEEE Transactions on Industrial Electronics, USA, v. 66, n. 4, p. 3237–3246, April 2019.
YEH, C.-C.; DEMERDASH, N. A. O. Induction motor-drive systems with fault tolerant inverter-motor capabilities. In: 2007 IEEE International Electric Machines & Drives Conference. USA: IEEE, 2007. v. 2, p. 1451–1458.
SINGH, G.; KAZZAZ, S. A. S. A. Induction machine drive condition monitoring and diagnostic research - a survey. Electric Power Systems Research, New York, USA, v. 64, n. 2, p. 145–158, February 2003.
KONAR, P.; CHATTOPADHYAY, P. Bearing fault detection of induction motor using wavelet and support vector machines (svms). Applied Soft Computing, Netherlands, v. 11, n. 6, p. 4203–4211, 2011.
ERTUNC, H.; OCAK, H.; ALIUSTAOGLU, C. Ann- and anfis-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Computing and Applications, London, United Kingdom, v. 22, n. 1, p. 435–446, May 2013.
GODOY, W. F. et al. Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Electric Power Applications, USA, v. 10, n. 5, p. 430–439, May 2016.
GODOY, W. F. et al. An application of artificial neural networks and pca for stator fault diagnosis in inverter-fed induction motors. In: 2016 XXII International Conference on Electrical Machines (ICEM). USA: IEEE, 2016. p. 2165–2171.
PALÁCIOS, R. H. C. et al. A novel multi-agent approach to identify faults in line connected three-phase induction motors. Applied Soft Computing, Netherlands, v. 45, p. 1–10, August 2016.
TRUJILLO-GUAJARDO, L. A. et al. A multiresolution taylor-kalman approach for broken rotor bar detection in cage induction motors. IEEE Transactions on Instrumentation and Measurement, USA, v. 67, n. 6, p. 1317–1328, June 2018.
IGLESIAS-MARTINEZ, M. E. et al. Detection of nonadjacent rotor faults in induction motors via spectral subtraction and autocorrelation of stray flux signals. IEEE Transactions on Industry Applications, USA, v. 55, n. 5, p. 4585–4594, September 2019.
DIAS, C. G.; Da Silva, L. C.; Alves, W. A. L. A histogram of oriented gradients approach for detecting broken bars in squirrel cage induction motors. IEEE Transactions on Instrumentation and Measurement, USA, v. 69, n. 9, p. 6968–6981, September 2020.
SABOURI, M. et al. Model-based unified technique for identifying severities of stator inter-turn and rotor broken bar faults in scims. IET Electric Power Applications, USA, v. 14, n. 2, p. 204–211, January 2020.
BENBOUZID, M. E. H. A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, USA, v. 47, n. 5, p. 984–993, October 2000.
ARABACI, H.; BILGIN, O. Automatic detection and classification of rotor cage faults in squirrel cage induction motor. Neural Computing and Applications, London, United Kingdom, v. 19, n. 5, p. 713–723, July 2010.
YAHIA, K. et al. Broken rotor bars diagnosis in an induction motor fed from a frequency converter: experimental research. International Journal of System Assurance Engineering and Management, London, United Kingdom, v. 3, n. 1, p. 40–46, March 2012.
EBRAHIMI, B. M. et al. Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform. Mechanical Systems and Signal Processing, United Kingdom, v. 30, n. 0, p. 131–145, July 2012.
SHI, P. et al. A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor. Mechanical Systems and Signal Processing, United Kingdom, v. 42, p. 388 – 403, January 2014.
NAHA, A. et al. A method for detecting half-broken rotor bar in lightly loaded induction motors using current. IEEE Transactions on Instrumentation and Measurement, USA, v. 65, n. 7, p. 1614–1625, July 2016.
GYFTAKIS, K. N. et al. Comparative experimental investigation of broken bar fault detectability in induction motors. IEEE Transactions on Industry Applications, USA, v. 52, n. 2, p. 1452–1459, March 2016.
ZAREI, J.; TAJEDDINI, M. A.; KARIMI, H. R. Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, United Kingdom, v. 24, n. 2, p. 151–157, March 2014.
BOSSIO, J. M.; ANGELO, C. H.; BOSSIO, G. R. Self-organizing map approach for classification of mechanical and rotor faults on induction motors. Neural Computing and Applications, London, United Kingdom, v. 23, n. 1, p. 41–51, July 2013.
SEERA, M. et al. Application of the fuzzy min max neural network to fault detection and diagnosis of induction motors. Neural Computing and Applications, London, United Kingdom, v. 23, n. 1, p. 191–200, December 2013.
SEERA, M. et al. Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model. Applied Soft Computing, Amsterdam, Netherlands, v. 13, n. 12, p. 4493–4507, December 2013.
TRAN, V. T. et al. An application to transient current signal based induction motor fault diagnosis of fourier bessel expansion and simplified fuzzy artmap. Expert Systems with Applications, United Kingdom, v. 40, n. 13, p. 5372–5384, October 2013.
PALÁCIOS, R. H. C. et al. A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electric Power Systems Research, Netherlands, v. 127, p. 249 – 258, October 2015.
PALÁCIOS, R. H. C. et al. Diagnosis of stator faults severity in induction motors using two intelligent approaches. IEEE Transactions on Industrial Informatics, USA, v. 13, n. 4, p. 1681–1691, August 2017.
LOPES, T. D. et al. Bearing fault identification of three-phase induction motors bases on two current sensor strategy. Soft Computing, Germany, v. 21, n. 22, p. 6673–6685, November 2017.
HAYKIN, S. O. Neural Networks and Learning Machines. 3. ed. USA: Prentice Hall, 2008.
How to Cite
Copyright (c) 2023 Rodrigo Henrique Cunha Palácios, Ivan Nunes da Silva, Wagner Fontes Godoy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.Autorizo aos editores a publicação de meu artigo, caso seja aceito, em meio eletrônico de acordo com as regras do Public Knowledge Project.