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International Symposium on Neural Networks

ISNN 2005: Advances in Neural Networks – ISNN 2005 pp 937–942Cite as

Fuzzy Support Vector Machine and Its Application to Mechanical Condition Monitoring

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

Abstract

Fuzzy support vector machine (FSVM) is applied in this paper, in order to resolve problem on bringing different loss for classification error to different fault type in mechanical fault diagnosis. Based on basic principle of FSVM, a method of determining numerical value range of fuzzy coefficient is proposed. Classification performance of FSVM is tested and verified by means of simulation data samples. A fuzzy fault classifier is constructed, and applied to condition monitoring of flue-gas turbine set. The results show that fuzzy coefficient can indicate importance degree of data sample, and classification error rate of important data sample can be decreased.

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References

  1. Lin, Y., Lee, Y., Wahba, G.: Support Vector Machines for Classification in Nonstandard Situations. Machine Learning 46, 199–202 (2002)

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  2. Ma, Y., Ding, X.: Face Detection Based on Cost-sensitive Support Vector Machines. In: Lee, S.W., Verri, A. (eds.) Pattern Recognition with Support Vector Machines, pp. 260–267. Springer, Berlin (2002)

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  3. Chun, F.C., Wang, S.D.: Fuzzy Support Vector Machines. IEEE Trans. On Neural Networks 13, 464–471 (2002)

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  4. IDA Benchmark Repository Used in Several Boosting, KFD and SVM papers, http://ida.first.gmd.de/~raetsch/data/benchmarks.htm

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Zhousuo Zhang, Qiao Hu & Zhengjia He

Authors
  1. Zhousuo Zhang
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  2. Qiao Hu
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  3. Zhengjia He
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Zhang, Z., Hu, Q., He, Z. (2005). Fuzzy Support Vector Machine and Its Application to Mechanical Condition Monitoring. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_150

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  • DOI: https://doi.org/10.1007/11427391_150

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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