Skip to main content
SpringerLink
Log in

Improved EMD Local Energy with SVM for Fault Diagnosis in Air Compressor

  • Conference paper
  • First Online:
Computational Intelligence: Theories, Applications and Future Directions - Volume II

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 799))

Abstract

The data generated from the machines are generally nonlinear and non-stationary in nature. Extracting relevant information from the data plays a major role in the fault diagnosis. This paper proposes local energy based feature extraction technique derived from improved empirical mode decomposition. Relevancy of feature is examined by correlation method. Support vector machine is used for classification of features. The proposed approach is compared with full signal energy using Hilbert transform on EMD. Improved empirical mode decomposition is used for decomposing acoustic signal into intrinsic mode function in lesser time compared to conventional EMD. Acoustic signals are acquired from most sensitive position of air compressor. Although acoustic signal-based machine health monitoring has not been applied to same extent as vibration signal. In this paper, acoustic signal has been used because of its advantage. The experimental results show the acceptable levels of average accuracy.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Verma, N.K., Sreevidya, K.: Cost benefit analysis for maintenance of rotating machines. In: IEEE International Conference on Prognostics and Health Management, pp. 1–7. Austin, USA, 22–25 Jun 2015

    Google Scholar 

  2. Verma, N.K., Ghosh, A., Dixit, S., Salour, A.: Cost-benefit and reliability analysis of prognostic health management systems using fuzzy rules. In: IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (IEEE WCI 2015), pp. 1–9. India, 14–17 Dec 2015

    Google Scholar 

  3. Li, R., He, D., Bechhoefe, E.: Investigation on fault detection for split torque gearbox using acoustic emission and vibration signals. In: Annual Conference of the Prognostics and Health Management Society, pp. 1–11 (2009)

    Google Scholar 

  4. Holroyd, T.J.: Acoustic Emission and Ultrasonics Monitoring Handbook. Coxmoor Publishing Company (2000)

    Google Scholar 

  5. Martin, H.R.: Statistical moment analysis as a means of surface damage detection. In: Proceedings of the International Modal Analysis Conference, pp. 1016–1021 (1989)

    Google Scholar 

  6. Peng, Y.H., Richard, Y.: Wavelet analysis and envelope detection for rolling element bearing fault diagnosis their effectiveness and flexibilities. J. Vib. Acoust. Trans. ASME 303–310 (2001)

    Google Scholar 

  7. Volker, E., Matin, H.R.: Application of kurtosis to damage mapping. In: Proceedings of the International Modal Analysis Conference, pp. 629–633 (1986)

    Google Scholar 

  8. Zheng, G.T., Wang, W.J.: A new cesptral analysis procedure of recovering excitations for transient components of vibration signals and applications to rotating machinery condition monitoring. J. Vib. Acoust. 123(2), 222–229 (2001)

    Article  Google Scholar 

  9. Verma, N.K., Kumar, P., Sevakula, R.K., Thirukovalluru, R.: Pattern analysis framework with graphical indices for condition based monitoring. IEEE Trans. Reliab. (2017) (Accepted)

    Google Scholar 

  10. Verma, N.K., Sharma, T., Maurya, S., Singh, D., Salour, A.: Real-time monitoring of machines using open platform communication. In: IEEE International Conference on Prognostics and Health Management (2017)

    Google Scholar 

  11. Benbouzid, M.E.H.: A review of induction motors signature analysis as a medium for faults detection. IEEE Trans. Ind. Electron. 47(5), 984–993 (2000)

    Article  Google Scholar 

  12. Henriquez, P., Alonso, J.B., Ferrer, M.A., Travieso, C.M.: Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans. Syst. Man. Cybern. 44(5), 642–652 (2014)

    Article  Google Scholar 

  13. Verma, N.K., Sevakula, R.K., Dixit, S., Salour, A.: Intelligent condition based monitoring using acoustic signals for air compressors. IEEE Trans. Reliab. 65(1), 291–309 (2016)

    Article  Google Scholar 

  14. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Workshop on Computational Learning Theory, pp. 144–152 (1992)

    Google Scholar 

  15. Verma, N.K., Kumar, P., Sevakula, R.K., Dixit, S., Salour, A.: Ranking of sensitive positions based on statistical parameters and cross correlation analysis. In: Sixth International Conference on Sensing Technology (ICST), pp. 815–821 (2012)

    Google Scholar 

  16. Verma, N.K., Kumar, P., Sevakula, R.K., Dixit, S., Salour, A.: Ranking of sensitive positions based on statistical parameters and cross correlation analysis. In: 6th IEEE International Conference on Sensing Technology, Kolkata, India, pp. 815–821 (2012)

    Google Scholar 

  17. Verma, N.K., Singh, N.K., Sevakula, R.K.: Ranking of sensitive positions using empirical mode decomposition and Hilbert transform. In: IEEE Conference on Industrial Electronics and Applications, pp. 1926–1931 (2014)

    Google Scholar 

  18. Verma, N.K., Sevakula, R.K., Dixit, S., Salour, A.: Ranking of sensitive positions using statistical and correlational analysis. Int. J. Smart Sens. Intell. Syst. 6(4), 1745–1762 (2013)

    Google Scholar 

  19. Verma, N.K., Jagannatham, K., Bahirat, A., Shukla, T.: Finding sensitive sensor positions under faulty condition of reciprocating air compressors. In: International Conference on IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, pp. 242–246 (2011)

    Google Scholar 

  20. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci. 1971(454), 903–995 (1998)

    Article  MathSciNet  Google Scholar 

  21. Du, Q., Yang, S.: Improvement of the EMD method and applications in defect diagnosis of ball bearings. Meas. Sci. Technol. 17(8), 2355–2361 (2006)

    Article  Google Scholar 

  22. Scholkopf, B., Smola, A.J.: Learning with Kernels, Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2002)

    Google Scholar 

  23. Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)

    Article  Google Scholar 

  24. Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 10551064 (1999)

    Article  Google Scholar 

  25. Stanislaw, O., Hoai, L.T., Markiewicz, T.: Support vector machine based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51(4), 582–589 (2004)

    Article  Google Scholar 

  26. Sapankevych, N.I., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2) (2009)

    Google Scholar 

  27. Vapnik, V., Cortes, C.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  28. Yin, S., Gao, X., Karimi, H.R., Zhu, X.: Study on support vector machine-based fault detection in Tennessee Eastman process. In: Abstract and Applied Analysis. Hindawi Publishing Corporation (2014)

    Google Scholar 

  29. Huang, J., Hu, X., Geng, X.: An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine. Electr. Power Syst. Res. 81(2), 400–407 (2011)

    Article  Google Scholar 

  30. Intelligent Data Engineering and Automation (IDEA) Laboratory, Indian Institute of Technology, Kanpur [online]. http://www.iitk.ac.in/idea/datasets/download-ACdata.php

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Seetaram Maurya, Vikas Singh, Narendra Kumar Dhar & Nishchal K. Verma

Authors
  1. Seetaram Maurya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikas Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Narendra Kumar Dhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nishchal K. Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seetaram Maurya .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Nishchal K. Verma

  2. Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    A. K. Ghosh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Maurya, S., Singh, V., Dhar, N.K., Verma, N.K. (2019). Improved EMD Local Energy with SVM for Fault Diagnosis in Air Compressor. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume II. Advances in Intelligent Systems and Computing, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-13-1135-2_7

Download citation

Publish with us

Policies and ethics

Search

Navigation