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Leakage Detection of a Boiler Tube Using a Genetic Algorithm-like Method and Support Vector Machines

  • Young-Hun Kim
  • Jaeyoung Kim
  • Jong-Myon KimEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)

Abstract

In this paper, we propose a method to detect boiler tube leakage using a genetic algorithm (GA)-like method and support vector machines (SVM). The GA-like method allows for selection of significant features, and the SVM detects a leak in boiler tubes using the selected features. Experimental results indicate that the proposed method outperforms a state-of-the-art principle component analysis (PCA) method in leakage detection.

Keywords

Boiler tube Genetic algorithm Support vector machine Tube leakage detection 

Notes

Acknowledgement

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20161120100350, No. 20181510102160, No. 20162220100050).

References

  1. 1.
    Lee, S.B., Roh, S.M.: Developing an early leakage detection system for thermal power plant boiler tubes by using acoustic emission technology. J. Korean Soc. Nondestr. Test. 38(2), 181–187 (2005)Google Scholar
  2. 2.
    Kim, J., Kim, J.: Methods and devices for diagnosing facility conditions, Patent Registration No. 10-1818394 (2018)Google Scholar
  3. 3.
    Kim, J., Kim, J.: Methods and devices for diagnosing machine faults, Patent Registration No. 10-1797402 (2018)Google Scholar
  4. 4.
    Kim, J., Kim, J.: Apparatus and method for machine fault diagnosis, Patent Registration No. 10-1745805 (2017)Google Scholar
  5. 5.
    Kim, J., Kim, J.: Machine fault diagnosis method, Patent Registration No. 10-1808390 (2017)Google Scholar
  6. 6.
    Kim, J., Kim, J.: Apparatus and method for monitoring machine condition, Patent Registration No. 10-1745805 (2017)Google Scholar
  7. 7.
    Kim, J., Kim, J.: Method and apparatus for predicting remaining life of a machine, Patent Registration No. 10-1808461 (2017)Google Scholar
  8. 8.
    Lee, K., Lee, B.W., Choi, D.-H., Kim, T.-O., Shin, D.: A study on fault detection monitoring and diagnosis system of CNG stations based on principal component analysis (PCA). J. Korean Inst. Gas 18(3), 53–59 (2014)CrossRefGoogle Scholar
  9. 9.
    Kang, M., Islam, M.R., Kim, J., Kim, J.-M., Pecht, M.: A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics. IEEE Trans. Ind. Electron. 63(5), 3299–3310 (2016)CrossRefGoogle Scholar
  10. 10.
    Kang, M., Kim, J., Wills, L.M., Kim, J.-M.: Time-varying and multiresolution envelope analysis and discriminative feature analysis for bearing fault diagnosis. IEEE Trans. Ind. Electron. 62(12), 7749–7761 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of UlsanUlsanSouth Korea
  2. 2.School of IT ConvergenceUniversity of UlsanUlsanSouth Korea

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