Induction Motor Fault Diagnosis Using Voltage Spectrum of Auxiliary Winding and Lissajous Curve of its Park Components

Abstract:

In this paper, a new method for induction motor fault diagnosis is presented. It is based on the so-called an auxiliary winding voltage and its Park components. The auxiliary winding is a small coil inserted between two of the stator phases. Expressions of the inserted winding voltage and its Park components are presented. After that, discrete Fourier transform analyzer is required for converting the signals from the time domain to the frequency domain. A Lissajous curve formed of the two Park components is associated to the spectrum. Simulation results curried out for non defected and defected motor show the effectiveness of the proposed method.

• Periodical: Advanced Materials Research (Volumes 805-806)
• Pages: 963-979
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.805-806.963
• Online since: September 2013
• Authors: Lamiaâ El Menzhi, Abdallah Saad
• Keywords: Auxiliary Winding Voltage, Diagnosis, Induction Motor (IM), Lissajous, Parkcomponents, Spectrum

References

[1] M. Farahani, H. Borsi and E. Gockenbach, Partial discharge and dissipation factor behaviour of model insulating systems for high voltage rotating machines under different stresses, IEEE Electrical Insulation Magazine, Vol. 21, No. 5, pp.5-19, Sep – Oct (2005).

DOI: 10.1109/mei.2005.1513425

[2] M.H. Benbouzid, Bibliography on induction motors faults detection and diagnosis, IEEE Transactions on Energy Conversion, Vol. 14, No. 4, pp.1065-1074, Dec (1999).

DOI: 10.1109/60.815029

[3] A.J.M. Cardoso, E.S. Saraiva, Computer-aided detection of airgap eccentricity in operating three-phase induction motors by park's vector approach, IEEE Transactions on Industry Applications, Vol. 29, No. 5, pp.897-901, Oct (1993).

DOI: 10.1109/28.245712

[4] A.J.M. Cardoso, E.S. Saraiva, M.L. Sousa Matens, M.L. Ranalho, On-line detection of airgap eccentricity in three-phase induction motors by park's vector approach, IEEE Transactions on Industry Applications, Vol. 29, No. 5, pp.897-901, Oct (1993).

DOI: 10.1109/28.245712

[5] H. Nejjari, M.E.H. Benbouzid, Monitoring and diagnosis of induction motors electrical faults using a current park's pattern learning approach, IEEE Transactions on Industry Applications, Vol. 36, No. 3, pp.730-735, May (2000).

DOI: 10.1109/28.845047

[6] F. Zidani, M.E.H. Benbouzid, D. Diallo, M.S. Nait Said, Induction motor stator faults diagnosis by a current concordia pattern-based fuzzy decision system, IEEE Transactions on Energy Conversion, Vol. 18, No. 4, pp.469-475, Dec (2003).

DOI: 10.1109/tec.2003.815832

[7] M.E.H. Benbouzid, A review of induction motors signature analysis as a medium for faults detection, IEEE Transactions on Industrial Electronics, Vol. 47, No. 5, pp.984-993, Oct (2000).

DOI: 10.1109/41.873206

[8] G. Didier, E. Ternisien, O. Caspary, H. Razik, Fault detection of droken rotor bars in induction motor using a global fault index, IEEE Transactions on Industry Applications, Vol. 42, No. 1, pp.79-88, Jan- Feb (2006).

DOI: 10.1109/tia.2005.861368

[9] H. Razik, Current spectrum analysis for a defected induction motor- state of the art, Published in 3EI, No. 29, pp.48-52, June 2002, France.

[10] L. El Menzhi, and A. Saad, Induction motor electrical fault diagnosis using voltage spectrum of an auxiliary winding, ICEMS 2007. Seoul South Korea, Vol 8, Issue, No 11, pp.1028-1032, Oct (2007).

DOI: 10.1109/icems12746.2007.4411959

[11] L. El Menzhi, and A. Saad, Induction motor electrical fault diagnosis using voltage spectrum of an auxiliary winding – Part 2, Conference Record of the 2008 IEEE, ISEI 2008, Vancouver Canada, Issue, 9-12 pp.620-623. Published 18 July (2008).

DOI: 10.1109/elinsl.2008.4570408