Skip to main content
SpringerLink
Log in

Feature extraction of induction motor stator fault based on particle swarm optimization and wavelet packet

  • Published:
Journal of Coal Science and Engineering (China)

Abstract

To effectively extract the interturn short circuit fault features of induction motor from stator current signal, a novel feature extraction method based on the bare-bones particle swarm optimization (BBPSO) algorithm and wavelet packet was proposed. First, according to the maximum inner product between the current signal and the cosine basis functions, this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO, which can eliminate the fundamental component and not affect other harmonic components. Then, the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor. Finally, the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Andreas S, Howard G S, James P, 2001. Current monitoring for detecting interturn short circuits in induction motors. IEEE Trans on Energy Conversion, 16(1): 32–37.

    Article  Google Scholar 

  • Bachir S, Tnani S, Trigeassou J C, Champenois G, 2006. Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Trans on Industrial Electronics, 53(3): 963–973.

    Article  Google Scholar 

  • Clerc M, Kennedy J, 2002. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1): 58–73.

    Article  Google Scholar 

  • Cao Z T, Chen H P, He G G, 2004. Support vector machine for fault diagnosis of the rotor broken bars of IM. Chinese Journal of Scientific Instrument, 25(6): 738–741.

    Google Scholar 

  • Cusido J, Rosero J A, Ortega J A, Garcia A, Romeral L, 2006. Induction motor fault detection by using wavelet decomposition on dq0 components. //IEEE ISIE 2006. Montreal: IEEE Press, 2406–2411.

    Google Scholar 

  • Fang F, Yang S Y, Hou X G, Wu Z G, 2009. Application of Park’s vector rotating transformation for stator fault diagnosis in induction motors. Proceedings of the CSEE, 29(12): 99–103.

    Google Scholar 

  • Hou X G, Wu Z G, Xia L, Bo L P, 2005. Application of instantaneous power decomposition technique in induction motors stator fault diagnosis. Proceedings of the CSEE, 25(5): 110–115.

    Google Scholar 

  • Kohler J L, Sottile J, Trutt F C, 1992. Alternatives for assessing the electrical integrity of induction motors. IEEE Trans on Industry Applications, 28(5): 1109–1117.

    Article  Google Scholar 

  • Kennedy J, Eberhart R C, 1995. Particle swarm optimization. //Proceedings of IEEE International Conference on Neural Networks. Piscataway: IEEE Press, 1942–1948.

    Chapter  Google Scholar 

  • Kennedy J, 2003. Bare bones particle swarms. In: Proceedings of IEEE Swarm Intelligence Symposium. Indiana: IEEE Press, 80–87.

    Google Scholar 

  • Nandi S, Toliyiat H A, 1999. Condition monitoring and fault diagnosis of electrical machines: a review. //Conference Record IEEE Industry Application Society Annual Meeting. Phoenix: IEEE Press, 197–204.

    Google Scholar 

  • Nandi S, 2006. Detection of stator faults in induction machines using residual saturation harmonics. IEEE Trans on Industry Applications, 42(5): 1201–1208.

    Article  Google Scholar 

  • Sérgio M A, Cruz A J, Marques C, 2005. Multiple reference frames theory: a new method for the diagnosis of stator faults in three-phase induction motors. IEEE Trans on Energy Conversion, 20(4): 871–882.

    Google Scholar 

  • Stocks M, Medvedev A, 2007. On-line estimation of all electrical parameters in induction machines subject to stator fault. //The 16th IEEE International Conference on Control Applications Part of Multi-conference on Systems and Control. Singapore: IEEE Press, 527–532.

    Google Scholar 

  • Xu B Q, Li H M, Sun L L, Wang Y N, 2004. Detection of stator winding inter-turn short circuit in induction motors. Proceedings of the CSEE, 24(7): 177–182.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou, 221008, China

    Pan-pan Wang, Li-ping Shi, Yong-jun Hu & Chang-xin Miao

Authors
  1. Pan-pan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li-ping Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong-jun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chang-xin Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pan-pan Wang.

Additional information

Supported by the Technological Research Fund of China’s Ministry of Education (311021)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, Pp., Shi, Lp., Hu, Yj. et al. Feature extraction of induction motor stator fault based on particle swarm optimization and wavelet packet. J Coal Sci Eng China 18, 432–437 (2012). https://doi.org/10.1007/s12404-012-0418-z

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12404-012-0418-z

Keywords

Search

Navigation