Fault Identification in the Stator Winding of Induction Motors Using PCA with Artificial Neural Networks

Journal of Control, Automation and Electrical Systems - Tập 27 Số 4 - Trang 406-418 - 2016
Rodrigo Henrique Cunha Palácios1, Alessandro Goedtel2, Wagner Fontes Godoy1, José Augusto Fabri2
1Department of Electrical Engineering, São Carlos School of Engineering, University of São Paulo, Av. Trabalhador São Carlense, 400, Centro, São Carlos, SP, 13.566-590, Brazil
2Departments of Computer and Electrical Engineering, Federal Technological University of Paraná, Cornélio Procópio, Brazil

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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.