Abstract
Three-phase induction motors are the main element of electrical into mechanical energy conversion applied in the industries. Due to its constant usage, added to adversities such as thermal, electrical and mechanical, these motors can be damaged causing unexpected process losses. Among the drawbacks of occurrences commonly presented for this equipment, approximately 37 % are related to short circuit in the stator coils. Hence, this article proposes an alternative approach for stator fault identification in induction motors through the discretization of the current signal, in the time domain, applying a variable optimization technique of principal components analysis (PCA) and artificial neural networks (ANNs) types multilayer perceptron (MLP) and radial basis function. Experimental results are presented with data gathered from an experimental workbench, considering various supply conditions and also under a wide load variation, by using the amplitude of the current signals in the time domain. Moreover, the MLP network presented the best results and the PCA technique provided a considerable reduction in the number of ANNs inputs, and in general, the classification results were comparable to the results obtained when the networks inputs considered the technique employing downsampling of 30 points to represent the current signals using half-cycle of the waveform.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Arabaci, H., & Bilgin, O. (2010). Automatic detection and classification of rotor cage faults in squirrel cage induction motor. Neural Computing and Applications, 19(5), 713–723.
Asfani, D., Muhammad, A., Syafaruddin, Purnomo M., & Hiyama, T. (2012). Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network. Expert Systems with Applications, 39(5), 5367–5375.
Bellini, A., Filippetti, F., Tassoni, C., & Capolino, G. A. (2008). Advances in diagnostic techniques for induction machines. IEEE Transactions on Industrial Electronics, 55(12), 4109–4126.
Bossio, J. M., Angelo, C. H., & Bossio, G. R. (2013). Self-organizing map approach for classification of mechanical and rotor faults on induction motors. Neural Computing and Applications, 23(1), 41–51.
Buhmann, M. D., & Buhmann, M. D. (2003). Radial Basis Functions. New York, NY: Cambridge University Press.
D’Angelo, M. F., Palhares, R. M., Takahashi, R. H., Loschi, R. H., Baccarini, L. M., & Caminhas, W. M. (2011). Incipient fault detection in induction machine stator-winding using a fuzzy-bayesian change point detection approach. Applied Soft Computing, 11(1), 179–192.
Ertunc, H., Ocak, H., & Aliustaoglu, C. (2013). Ann- and anfis-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Computing and Applications, 22(1), 435–446.
Gao, X., Wang, X., & Zenger, K. (2014). Motor fault diagnosis using negative selection algorithm. Neural Computing and Applications, 25(1), 55–65.
García-Escudero, L. A., Duque-Perez, O., Morinigo-Sotelo, D., & Perez-Alonso, M. (2011). Robust condition monitoring for early detection of broken rotor bars in induction motors. Expert Systems with Applications, 38(3), 2653–2660.
Georgoulas, G., Mustafa, M., Tsoumas, I., Antonino-Daviu, J., Climente-Alarcon, V., Stylios, C., et al. (2013). Principal component analysis of the start-up transient and hidden markov modeling for broken rotor bar fault diagnosis in asynchronous machines. Expert Systems with Applications, 40(17), 7024–7033.
Godoy, W. F., da Silva, I. N., Goedtel, A., & Palácios, R. H. C. (2015). Evaluation of stator winding faults severity in inverter-fed induction motors. Applied Soft Computing, 32, 420–431.
Haykin, S. (1998). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River, NJ: Prentice Hall PTR.
Haykin, S. (2008). Neural networks and learning machines (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York: Springer.
Konar, P., & Chattopadhyay, P. (2011). Bearing fault detection of induction motor using wavelet and support vector machines (svms). Applied Soft Computing, 11(6), 4203–4211.
Kurek, J., & Osowski, S. (2010). Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor. Neural Computing and Applications, 19(4), 557–564.
Li, P., Li, X., Jiang, L., & Cao, Y. (2014). Fault diagnosis of asynchronous motor based on kpca and psosvm. Journal of Vibration, Measurement and Diagnosis, 34(4), 616–620.
Nascimento, C. F., de Oliveira Jr, A. A., Goedtel, A., & Serni, P. J. A. (2011). Harmonic identification using parallel neural networks in single-phase systems. Applied Soft Computing, 11(2), 2178–2185.
Palácios, R. H. C., da Silva, I. N., Goedtel, A., Godoy, W. F., & Oleskovicz, M. (2014). A robust neural method to estimate torque in three-phase induction motor. Journal of Control, Automation and Electrical Systems, 25(4), 493–502.
Palácios, R. H. C., da Silva, I. N., Goedtel, A., & Godoy, W. F. (2015). A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electric Power Systems Research, 127, 249–258.
Seera, M., Lim, C., Ishak, D., & Singh, H. (2013a). Application of the fuzzy min max neural network to fault detection and diagnosis of induction motors. Neural Computing and Applications, 23(1), 191–200.
Seera, M., Lim, C. P., Ishak, D., & Singh, H. (2013b). Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model. Applied Soft Computing, 13(12), 4493–4507.
Silva, I. N., Spatti, D. H., & Flauzino, R. A. (2010). Artificial neural networks for engineering and applied sciences (in Portuguese). São Paulo: ArtLiber.
Su, H., Chong, K., & Ravi Kumar, R. (2011). Vibration signal analysis for electrical fault detection of induction machine using neural networks. Neural Computing and Applications, 20(2), 183–194.
Tran, V. T., AlThobiani, F., Ball, A., & Choi, B. K. (2013). An application to transient current signal based induction motor fault diagnosis of fourier bessel expansion and simplified fuzzy artmap. Expert Systems with Applications, 40(13), 5372–5384.
Uddin, J., Kang, M., Nguyen, D., Kim, J. M. (2014). Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine. Mathematical Problems in Engineering 2014
Wang, J., Liu, S., Gao, R. X., & Yan, R. (2012). Current envelope analysis for defect identification and diagnosis in induction motors. Journal of Manufacturing Systems, 31(4), 380–387.
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014). Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics, 24(2), 151–157.
Acknowledgments
The authors gratefully acknowledge the contributions of CNPq (Process #552269/2011-5), FAPESP (Process #2011/17610-0), Araucária Foundation/CAPES (CP 13/2014), University of São Paulo and Federal Technological University of Paraná for their financial support toward the development of this research.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Palácios, R.H.C., Goedtel, A., Godoy, W.F. et al. Fault Identification in the Stator Winding of Induction Motors Using PCA with Artificial Neural Networks. J Control Autom Electr Syst 27, 406–418 (2016). https://doi.org/10.1007/s40313-016-0248-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40313-016-0248-0