Abstract
This paper describes an application about detection of bearing defects in inverter fed induction motors, using Concordia transform approach based algorithm. After introduction, brief information is given about bearing structure and type of bearing failures. Next section, Concordia transform theory is mentioned then, RBF neural network structure is summarized. After that, test system information is specified. This paper indicates that Concordia transform approach is a reliable tool to detect bearing faults in inverter fed small induction motors. The generality of the proposed methodology has been experimentally tested on a 1 HP squirrel-cage induction motor. At the end of the paper, an ANN algorithm is proposed that could detect the bearing faults automatically. The obtained results have 93.75% accuracy. This study suggests that proposed Concordia transform based fault detection algorithm could be integrated in an induction motor driver so, bearing condition of the induction motor could be observed while motor is working and bearing faults could be detect before they become serious.
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References
Benbouzid M.E.H. et al (1999) Induction motor’ fault detection and localisation using stator current advanced signal processing techniques. IEEE Transactions on Power Electronics 14(1): 14–22
Benbouzid M.E.H. et al (2003) Induction motor stator faults diagnosis by a current Concordia pattern based fuzzy decision system. IEEE Transactions on Energy Conversion 18(4): 469–475
EMRC (Electric Motors Reference Center) web site. (2007). Induced bearing currents. http://www.electricmotors.machinedesign.com.
Eschmann P. et al (1958) Ball and roller bearings: Their theory, design, and application. K. G. Heyden, London
Greenheck Fan corporation website. (2007). Application guides. http://www.greenheck.com/.
Leonard J.A., Kramer M.A. (1991) Radial basis function networks for classifying process faults. IEEE Control Systems Magazine 11(3): 31–38
Nejjari H., Benbouzid M.E.H. (2000) Monitoring and diagnosis of induction motors electrical faults using a current Park’s vector pattern learning approach. IEEE Transactions on Industry Applications 36(3): 730–735
Obaid, R., et al. (2003). Stator current analysis for bearing damage detection in induction motors. In SDEMPED 2003, Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Atlanta, GA, USA, August 24–26, 2003.
Önel İ.Y., Dalcı K.B., şenol İ. (2005) Detection of outer raceway bearing defects in small induction motors using stator current analysis. Sadhana 30(Part 6): 713–722
Reliance Electric Company. (2000). CurrentShield TM technology—motors and drives for cleanroom application. http://www.reliance.com.
Schoen Randy R. et al (1995) Motor bearing damage detection using stator current monitoring. IEEE Transactions on Industrial Applications 31(6): 1274–1279
Zidani F. et al (2003) Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system. IEEE Transactions on Energy Conversion 18(4): 469–475
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Önel, İ.Y., Ayçiçek, E. & Şenol, İ. An experimental study, about detection of bearing defects in inverter fed small induction motors by Concordia transform. J Intell Manuf 20, 243–247 (2009). https://doi.org/10.1007/s10845-008-0234-x
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DOI: https://doi.org/10.1007/s10845-008-0234-x