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An Application of Support Vector Machines for Induction Motor Fault Diagnosis with Using Genetic Algorithm

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Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5227))

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Abstract

This paper introduces a technique for diagnosing mechanical faults of induction motors by using support vector machine (SVM) and genetic algorithm (GA). Features are extracted from the vibration time signals and selected by using GA with a distance evaluation fitness function. All SVM parameters are also obtained simultaneously by the same GA. The SVM is studied with two types of kernel functions, the radial basis function and the polynomial function. Four motor conditions are investigated with the chosen SVM classifiers. The classification results have high accuracy for the chosen feature set and SVM parameters.

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References

  1. Widodo, A., Yang, B.S., Han, T.: Combination of Independent Component Analysis and Support Vector Machines for Intelligent Faults Diagnosis of Induction Motors. Expert Systems with Applications 32, 299–312 (2007)

    Article  Google Scholar 

  2. Yuan, S.F., Chu, F.L.: Fault Diagnostics Based on Particle Swarm Optimization and Support Vector Machines. Mechanical Systems and Signal Processing 21, 1787–1798 (2007)

    Article  Google Scholar 

  3. Yang, Y., Yu, D., Cheng, J.: A Fault Diagnosis Approach for Roller Bearing Based on IMF Envelope Spectrum and SVM. Measurement 40, 943–950 (2007)

    Article  Google Scholar 

  4. Jack, L.B., Nandi, A.K.: Fault Detection Using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms. Mechanical Systems and Signal Processing 16, 373–390 (2002)

    Article  Google Scholar 

  5. Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A.: Artificial Neural Networks and Support Vector Machines with Genetic Algorithm for Bearing Fault Detection. Engineering Applications of Artificial Intelligence 16, 657–665 (2003)

    Article  Google Scholar 

  6. Sugumaran, V., Muralidharan, V., Ramachandran, K.I.: Feature Selection Using Decision Tree and Classification through Proximal Support Vector Machine for Fault Diagnostics of Roller Bearing. Mechanical Systems and Signal Processing 21, 930–942 (2007)

    Article  Google Scholar 

  7. Lei, Y., He, Z., Zi, Y., Hu, Q.: Fault Diagnosis of Rotating Machinery Based on Multiple ANFIS Combination with Gas. Mechanical Systems and Signal Processing 21, 2280–2294 (2007)

    Article  Google Scholar 

  8. Ye, Z., Sadeghian, A., Wu, B.: Mechanical Fault Diagnostics for Induction Motor with Variable Speed Drives Using Adaptive Neuro-fuzzy Inference System. Electric Power Systems Research 76, 742–752 (2006)

    Article  Google Scholar 

  9. Sun, W., Chen, J., Li, J.: Decision Tree and PCA-based Fault Diagnosis of Rotating Machinery. Mechanical Systems and Signal Processing 21, 1300–1317 (2007)

    Article  Google Scholar 

  10. Samanta, B., Al-Balushi, K.R., Al-Araimi, S.A.: Artificial Neural Networks and Genetic Algorithm for Bearing Fault Detection. Soft Comput. 10, 264–271 (2006)

    Article  Google Scholar 

  11. Casimir, R., Boutleux, E., Clerc, G., Yahoui, A.: The Use of Features Selection and Nearest Neighbors Rule for Faults Diagnostic in Induction Motors. Engineering Applications of Artificial Intelligence 19, 169–177 (2006)

    Article  Google Scholar 

  12. Rao, J.S.: Vibratory Condition Monitoring of Machines, pp. 361–382. Alpha Science International Ltd, UK (2000)

    Google Scholar 

  13. Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm

  14. Hsu, C.W., Chang, C.C., Lin, C.J.: A Practical Guide to Support Vector Classification, http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

  15. Support vector machine, http://en.wikipedia.org/wiki/Support_vector_machine

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Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Ngoc-Tu Nguyen & Hong-Hee Lee

Authors
  1. Ngoc-Tu Nguyen
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  2. Hong-Hee Lee
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Editor information

De-Shuang Huang Donald C. Wunsch II Daniel S. Levine Kang-Hyun Jo

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© 2008 Springer-Verlag Berlin Heidelberg

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Nguyen, NT., Lee, HH. (2008). An Application of Support Vector Machines for Induction Motor Fault Diagnosis with Using Genetic Algorithm. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_24

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  • DOI: https://doi.org/10.1007/978-3-540-85984-0_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85983-3

  • Online ISBN: 978-3-540-85984-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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