Abstract
The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces a technique to detect faults in induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is used to display the signals. Fourier transform is used to convert the signals onto frequency domain. After the signals have been converted, the features of the signals are extracted by the signal processing methods like the wavelet analysis, spectrum analysis, and other methods. The discovered features are entered to a pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, or other models. This paper describes the results of detecting fault using Fourier and wavelet analysis.
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References
Vas, P.: Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines. Clarendron Press, Oxford (1993)
Kliman, G.B., Stein, J.: Induction motor fault detection via passive current monitoring. In: International Conference in Electrical Machines, Cambridge, MA, August 1990, pp. 13–17 (1990)
Abbaszadeh, K., Milimonfared, J., Haji, M., Toliyat, H.A.: Broken Bar Detection In Iduction Motor via Wavelet Transformation. In: IECON 2001: The 27th Annual Conference of the IEEE Industrial Electronics Society, pp. 95–99 (2001)
Zhongming, Y.E., Bin, W.U.: A Review on Induction Motor Online Fault Diagnosis. In: The Third International Power Electronics and Motion Control Conference (PIEMC 2000), August 15-18, vol. 3, pp. 1353–1358 (2000)
Haji, M., Toliyat, H.A.: Patern Recognition-A Technique for Induction Machines Rotor Fault Detection Eccentricity and Broken Bar Fault. In: Conference Record of the 2001 IEEE Industry Applications Conference, September 30-October 4, vol. 3, pp. 1572–1578 (2001)
Nandi, S., Toliyat, H.A.: Condition Monitoring and Fault Diagnosis of Electrical Machines – A Review. In: IEEE Industry Applications Conference, vol. 1, pp. 197–204 (1999)
Yazici, B., Kliman, G.B.: An Adaptive Statistical Time-Frequency Method for Detection of Broken Bars and Bearing Faults in Motors Using Stator Current. IEEE Trans. On Industry Appl. 35(2), 442–452 (1999)
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© 2005 Springer-Verlag Berlin Heidelberg
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Bae, H., Kim, S., Kim, Y.T., Lee, SH. (2005). Application of Time-Series Data Mining for Fault Diagnosis of Induction Motors. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424925_113
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DOI: https://doi.org/10.1007/11424925_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25863-6
Online ISBN: 978-3-540-32309-9
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