Abstract
Deterioration in mechanical parts of a motor causes faults that generate vibrations. Those vibrations can be related with a different type of motor fault. In this work, we propose a new computational model for identifying rotor unbalance problems in electrical induction motors. Measured vibrations are preprocessed in order to create orbits which represent characteristic patterns. Those patterns are used in a recognition process using an artificial neural network. Experimental results using vibration signals extracted from real situations show a good performance and effectiveness of the proposed model, providing a new way for recognizing unbalance problems in induction motors.
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Gaylard, A., Meyer, A., Landy, C.: Acoustic evaluation of faults in electrical machines. In: Electrical Machines and Drives, Conference Publication, vol. 412, pp. 147–150 (1995)
Yahya, L., Saleem, T., Hasan, B.: The application of Neural Network to Electrical Motor’s Sound Recognition System. Journal of Engineering and Applied Sciences 7(2), 191–193 (2012)
Ha, K., Hong, J., Kim, G., Chang, K., Lee, J.: Orbital analysis of rotor due to electromagnetic force for switched reluctance motor. IEEE Transactions on Magnetics 36(4), 1407–1411 (2000)
Iorgulescu, M., Beloiu, R., Cazacu, D.: Vibration monitoring for electrical equipment faults detection using fast fourier transform. In: Proceedings of the 1st International Conference on Manufacturing Engineering, Quality and Production Systems, vol. 1, pp. 34–38 (2009)
Kim, K., Parlos, A.: Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Transactions on Mechatronics 7(2), 201–219 (2002)
Climente, V., Antonino, J., Riera, M., Puche, R., Escobar, L.: Application of the Wigner–Ville distribution for the detection of rotor asymmetries and eccentricity through high-order harmonics. Electric Power Systems Research 91, 28–36 (2012)
Liu, D., Zhao, Y., Yang, B., Sun, J.: A new motor fault detection method using multiple window S-method time-frequency analysis. In: International Conference on Systems and Informatics, pp. 2563–2566 (2012)
Yang, D.: Induction motor bearing fault diagnosis using Hilbert-based bispectral analysis. In: International Symposium on Computer, Consumer and Control, IS3C, Taichung, Taiwan (2012)
Zhen, D., Wang, T., Gu, F., Ball, A.: Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping. Mechanical Systems and Signal Processing 34(1-2), 191–202 (2013)
Chow, M.: Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection. WorldScientific, Singapore (1997)
Banerjee, T., Das, S.: Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences 217, 96–107 (2012)
Gardel, P., Morinigo, D., Duque, O., Pérez, M., Garcia.: Neural network broken bar detection using time domain and current spectrum data. In: Proceedings of the 20th International Conference on Electrical Machines, No. 6350234, pp. 2492–2497 (2012)
Liang, B., Iwnicki, S., Zhao, Y.: Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis. Mechanical Systems and Signal Processing 39(1-2), 342–360 (2013)
Sun, C., Duan, Z., Yang, Y., Wang, M., Hu, L.: The motor fault diagnosis based on neural network and the theory of D-S evidence. Advanced Materials Research 683, 881–884 (2013)
Zidani, F., Benbouzid, M., Diallo, D., Naït, M.: Induction Motor Stator Faults Diagnosis by a Current Concordia Pattern-Based Fuzzy Decision System. IEEE Transactions on Energy Conversion 18(4), 469–475 (2003)
Palomino, E., Sánchez, A., Cabrera, J., Sexto, L.: Preliminary Diagnosis of Rotational Machinery. In: Experiences in the Implementation of a Predictive Maintenance Program and Certification of Human Resources in a Cuban Cement Industry. Advances in Vibration Control and Diagnostics, pp. 177–184. Polimetrica International Scientific Publisher (2006)
Arun, K., Mohanty, A.: Model based fault diagnosis of a rotor–bearing system for misalignment and unbalance under steady-state condition. Journal of Sound and Vibration 327(3-5), 604–622 (2009)
Chen, F., Jhe, S., Min, P., Wen, T.: Study of start-up vibration response for oil whirl, oil whip and dry whip. Mechanical Systems and Signal Processing, Elsevier 25(8), 3102–3115 (2011)
Han, S.: Retrieving the time history of displacement from measured acceleration signal. Journal of Mechanical Science and Technology 17(2), 197–206 (2003)
ISO 10816. Mechanical vibration: evaluation of machine vibration by measurements on non-rotating parts (1995)
VDI 2056. Standards of evaluation for mechanical vibrations of machines, Germany (1964)
Dongfeng, S., Lianfsheng, O., Ming, B.: Instantaneous purified orbit: a new tool for analysis of nonstationary vibration of rotor system. International Journal of Rotating Machinery 7(2), 105–115 (2001)
Proakis, J., Manolakis, D.: Tratamiento digital de señales, 4a edn. Pearson Education, vol. 1. España (2007)
Orfanadis, S.: Introduction to Signal Processing. Prentice Hall (2009)
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Carbajal-Hernández, J.J., Sánchez-Fernández, L.P., Suárez-Guerra, S., Hernández-Bautista, I. (2014). Rotor Unbalance Detection in Electrical Induction Motors Using Orbital Analysis. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_38
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DOI: https://doi.org/10.1007/978-3-319-07491-7_38
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