Abstract
Three-phase squirrel cage induction motor being a core component of industrial drives needs fault detection strategies which can detect internal faults in very early stage of its development. This can result in enormous financial saving in industries. Simulation studies with suitable mathematical models helps in identification of fault signatures in the diagnostic signal. The work presented in this paper addresses the issue of detection of incipient static eccentricity faults. Modelling of motor with static eccentricity fault is done and characteristic signatures were identified in frequency spectrum of stator current. These components were also identified in the vibration spectrum, by conducting a practical experimentation in three-phase squirrel cage induction motor with fabricated static eccentricity. The results validates the modelling approach and also demonstrates the suitability of vibration and stator current signal for the diagnosis of incipient static eccentricity faults.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, P., Du, Y., Habetler, T.G., Lu, B.: A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans. Ind. Appl. 47, 34–46 (2011)
Thomas, V.V., Vasudevan, K., Kumar, V.J.: Online cage rotor fault detection using air-gap torque spectra. IEEE Trans. Energy Convers. 18, 265–270 (2003)
Hyun, D., Hong, J., Lee, S.B., Kim, K., Wiedenbrug, E.J., Teska, M., Nandi, S., Chelvan, I.T.: Automated monitoring of air gap eccentricity, for inverter-fed induction motors under standstill conditions. IEEE Trans. Ind. Appl. 47, 1257–1266 (2011)
Thomas, V.V., Bindu, S.: Non-invasive techniques for fault diagnoses of induction machines. In: Proceedings of Third International Engineering Symposium (2013)
Bindu, S., Thomas, V.V.: Diagnoses of internal faults of three phase squirrel cage induction motor—a review. In: Proceedings of ICAECT-2014, pp. 48–54, (2014)
Huang, X., Habetler, T.G., Harley, R.G.: Detection of rotor eccentricity faults in a closed-loop drive-connected induction motor using an artificial neural network. IEEE Trans. Power Electron. 22, 1552–1559 (2007)
Krishnan, R.: Electric Motor Drives: Modelling, Analysis and Control, Pearson (2011)
Kamal, V.A., Giri, K.: Mathematical modelling of dynamic induction motor and performance analysis with bearing fault. Proc. Int. J. Innov. Technol. Res. 1, 336–340 (2013)
Kamal, A., Giri, V.K.: Detection of bearing fault in three phase induction motor using wavelet. VSRD J. Electr. Electron. Commun. Eng. 3 (2013)
Luo, X., Liao, Y., Toliat, H.A., El-Antably, A., Lipo, T.A.: Multiple coupled circuit modeling of induction machines. IEEE Trans. Ind. Appl. 31, 311–318 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Bindu, S., Thomas, V.V. (2018). Detection of Static Air-Gap Eccentricity in Three-Phase Squirrel Cage Induction Motor Through Stator Current and Vibration Analysis. In: Garg, A., Bhoi, A., Sanjeevikumar, P., Kamani, K. (eds) Advances in Power Systems and Energy Management. Lecture Notes in Electrical Engineering, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-4394-9_50
Download citation
DOI: https://doi.org/10.1007/978-981-10-4394-9_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4393-2
Online ISBN: 978-981-10-4394-9
eBook Packages: EnergyEnergy (R0)