Indian Journal of Science and Technology

Open Access Open Access  Restricted Access Subscription Access

SVM Based Fault Classification of Three Phase Induction Motor


Affiliations
1 Electrical Engineering Department, Government College of Engineering, Amravati (MS), India
2 Applied Electronics Department, Sant Gadge Baba Amravati University, Amravati
 

Early detection of abnormal conditions during induction motor's operation would eliminate consequential damages on motor, so that outage time and costs of repairing can be reduced. Due to unique fingerprints from faults in line currents, it is possible to detect faults by extracting fault information from line currents. From the literature review it is observed that in many model based on ANN based techniques, the stator current spectra is used as input feature space. In this paper, simple thirteen statistical parameters are used as an input feature space. Support Vector Machine (SVM) is proposed as a fault classifier. Since the basic SVM is essentially a 2-class classifier, the synergism of three classifiers is proposed to overcome the limitation. Principal Component Analysis (PCA) is used as data fusion method to reduce the dimension of classifier. To verify the performance various kernel function as (Radial Basis Function (RBF), Quadratic, Linear, Polynomial, Multilayer Perceptron (MLP)) are applied and tested with real experimental datasets. In order to generate the experimental data, specially designed 2 HP, three phase, 4 pole, 415V, 50 Hz induction motor is used.

Keywords

Induction Motor, Fault Classification, SVM, PCA
User

  • Burges C (1998) A Tutorial on Support Vector Machines for Pattern Recognition. J. data Mining & Knowledge Discovery. 2(2), 121-167.

  • Chow M -Y, Sharpe RN and Hung JC (1993) On the application and design consideration of artificial neural network fault detectors. IEEE Trans. Ind. Electron. 40, 181–198.

  • Cristianini N and Shawe-Taylo, J (2000) Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press.

  • Filippetti F, Franceschini G, Tassoni C and Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans. Ind. Electron. 47, 994–1004.

  • Isermann R (1997) Supervision, fault-detection and fault-diagnosis methods-An introduction. Control Eng. Practice, 5 (5), 639–652.

  • Jarmo Ilonen and Joni-Kristian Kamarainen (2005) Diagnosis Tool for Motor Condition Monitoring. IEEE Trans. Industry Appln. 41 (4), 963-971.

  • Kecman V (2001) Learning and Soft Computing; Support Vector Machines. Neural Networks and Fuzzy Logic Models. The MIT Press.

  • Onel IY and EI Hachemi Benbouzid M (2008) Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study. IEEE Trans. Mechatronics. 13, 257-262.

  • Singh GK and Al Kazzaz SAS (2003) Induction machine drive condition monitoring and diagnostic research- a survey. Electric Power Systems Res. 64 (2), 145–158.

  • Tian Han and Bo-Suk Yang (2006) Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals. Hindawi Publ. Corpn. Intnl. J. Rotating Machinery. 1,1-13.

  • Vapnik VN (2000) The Nature of Statistical Learning Theory. Springer-Verlag, New York.


Abstract Views: 380

PDF Views: 81




  • SVM Based Fault Classification of Three Phase Induction Motor

Abstract Views: 380  |  PDF Views: 81

Authors

V. N. Ghate
Electrical Engineering Department, Government College of Engineering, Amravati (MS), India
S. V. Dudul
Applied Electronics Department, Sant Gadge Baba Amravati University, Amravati

Abstract


Early detection of abnormal conditions during induction motor's operation would eliminate consequential damages on motor, so that outage time and costs of repairing can be reduced. Due to unique fingerprints from faults in line currents, it is possible to detect faults by extracting fault information from line currents. From the literature review it is observed that in many model based on ANN based techniques, the stator current spectra is used as input feature space. In this paper, simple thirteen statistical parameters are used as an input feature space. Support Vector Machine (SVM) is proposed as a fault classifier. Since the basic SVM is essentially a 2-class classifier, the synergism of three classifiers is proposed to overcome the limitation. Principal Component Analysis (PCA) is used as data fusion method to reduce the dimension of classifier. To verify the performance various kernel function as (Radial Basis Function (RBF), Quadratic, Linear, Polynomial, Multilayer Perceptron (MLP)) are applied and tested with real experimental datasets. In order to generate the experimental data, specially designed 2 HP, three phase, 4 pole, 415V, 50 Hz induction motor is used.

Keywords


Induction Motor, Fault Classification, SVM, PCA

References





DOI: https://doi.org/10.17485/ijst%2F2009%2Fv2i4%2F29427