A New Method for Identifying Broken Rotor Bars in Squirrel Cage Induction Motor Based on Particle Swarm Optimization Method

Detection of squirrel cage induction motor (SCIM) broken bars has long been an important but difficult job in the detection area of motor faults. Early detection of this abnormality in the motor would help to avoid costly breakdowns. A new detection method based on particle swarm optimization (PSO) is presented in this paper. Stator current in an induction motor will be measured and characteristic frequency components of faylted rotor will be detected by minimizing a fitness function using pso. Supply frequency and side band frequencies and their amplitudes can be estimated by the proposed method. The proposed method is applied to a faulty motor with one and two broken bars in different loading condition. Experimental results prove that the proposed method is effective and applicable.

• V. Rashtchi
• R. Aghmasheh

• broken bar
• PSO
• fault detection
• SCIM

[1] Bulent Ayhan, Mo-Yuen Chow, Myun-Hung Song, "Multiple
Signature Processing-Based Fault Detection Schemes for Broken
Bar in Induction Motors," IEEE Trans. Energy Conversion,
vol.20, No.2, June 2005
[2] William T. Thosmson, Mark Fenger, "Current Signature Analysis
to Detect Induction motor Faults," IEEE Industry Application
Magazine, July/August 2001
[3] Guillermo A.Jimenez, Alfredo O.Munoz, "Fault detection in
induction motors using Hilbert and Wavelet transforms,"
Springer-Verlag 2006
[4] Zhengping Zhang, Zhen Ren, "A Novel Detection Method of
Motor Broken Rotor Bars on Wavelet Ridge," IEEE Trans. Energy
Conversion, vol.18, No.3, Sep.2003
[5] Bin Lu, Manish Paghda, "Induction Motor Fault Diagnosis Using
Wavelet Analysis of One-Cycle Average Power," IEEE 2008
[6] Bulent Ayhan, Mo-Yuen Chow, "Multiple Discriminant Analysis
and Neural-Network-Based Monolith and Partition Fault-
Detection Schemes for Broken Bar in Induction Motors," IEEE
Trans. Industrial Electronics, vol.53, No.4, AUGUST 2006
[7] J. Kennedy and R. Eberhart, "Particle swarm optimization," in
Proc.IEEE Int. Conf. Neural Netw., vol. 4, Nov. 1995, pp. 1942-
1948.
[8] J. Kennedy and R. Mendes, "Neighborhood topologies in fullyinformed
and best-of-neighborhood particle swarms," Proc. of the
IEEE International Workshop, pp. 45-50, June 2003.
[9] R. Eberhart and Y. Shi, "Particle swarm optimization:
developments, applications and resources," in Proc. Cong. Evol.
Comput, Vol. 1, pp. 81-86, 2001.
[10] A.Raei, V.Rashtchi, " Accurate identification of parameters, in
winding function model of induction motor, using genetic
algorithm", SICE 2002 August 5-7,Osaka