SpringerLink
Log in

Comparison of Health Indicators Construction for Concrete Structure Using Acoustic Emission Hit and Kullback-Leibler Divergence

  • Conference paper
  • First Online:
Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1688))

Included in the following conference series:

  • 1822 Accesses

Abstract

This paper investigates the construction of health indicators (HIs) for concrete structures using acoustic emission (AE) hit and Kullback-Leibler Divergence (KLD). Health indicator has an important role in the structural health monitoring (SHM) framework through its portrayal of the deterioration process. By harnessing AE nondestructive test, the authors suggest that the HI can be constructed through a deep learning model from the raw data. Prior to the training of the deep neural network (DNN), its parameters are achieved by autoencoder pretraining and fine-tuning. Afterwards, the AE hits and KLD values are extracted from the data to be the training label for two different types of HI. The evaluation of two HIs are done with fitness analysis and remaining useful lifetime (RUL) prognosis, which shows both their capability to present the deterioration process and their drawback in regard to this matter.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ohtsu, M., Isoda, T., Tomoda, Y.: Acoustic emission techniques standardized for concrete structures. J. Acoust. Emission 25, 21–32 (2007)

    Google Scholar 

  2. Sagar, R.V., Prasad, B.K.R., Kumar, S.S.: An experimental study on cracking evolution in concrete and cement mortar by the b-value analysis of acoustic emission technique. Cem. Concr. Res. 42(8), 1094–1104 (2012). https://doi.org/10.1016/j.cemconres.2012.05.003

    Article  Google Scholar 

  3. Wang, J., Basheer, P.A.M., Nanukuttan, S.V., Long, A.E., Bai, Y.: Influence of service loading and the resulting micro-cracks on chloride resistance of concrete. Constr. Build. Mater. 108, 56–66 (2016). https://doi.org/10.1016/j.conbuildmat.2016.01.005

    Article  Google Scholar 

  4. Han, Q., Yang, G., Xu, J., Fu, Z., Lacidogna, G., Carpinteri, A.: Acoustic emission data analyses based on crumb rubber concrete beam bending tests. Eng. Fract. Mech. 210, 189–202 (2019). https://doi.org/10.1016/j.engfracmech.2018.05.016

    Article  Google Scholar 

  5. Wolf, J., Pirskawetz, S., Zang, A.: Detection of crack propagation in concrete with embedded ultrasonic sensors. Eng. Fract. Mech. 146, 161–171 (2015). https://doi.org/10.1016/j.engfracmech.2015.07.058

    Article  Google Scholar 

  6. Ohno, K., Ohtsu, M.: Crack classification in concrete based on acoustic emission. Constr. Build. Mater. 24(12), 2339–2346 (2010). https://doi.org/10.1016/j.conbuildmat.2010.05.004

    Article  Google Scholar 

  7. Aggelis, D.G., Mpalaskas, A.C., Matikas, T.E.: Investigation of different fracture modes in cement-based materials by acoustic emission. Cem. Concr. Res. 48, 1–8 (2013). https://doi.org/10.1016/j.cemconres.2013.02.002

    Article  Google Scholar 

  8. Abarkane, C., Rescalvo, F.J., Donaire-Ávila, J., Galé-Lamuela, D., Benavent-Climent, A., Molina, A.G.: Temporal acoustic emission index for damage monitoring of RC structures subjected to bidirectional seismic loadings. Materials 12(17), 2804 (2019). https://doi.org/10.3390/ma12172804

    Article  Google Scholar 

  9. Moctezuma, F.P., Prieto, M.D., Martinez, L.R.: Performance analysis of acoustic emission hit detection methods using time features. IEEE Access 7, 71119–71130 (2019). https://doi.org/10.1109/ACCESS.2019.2919224

    Article  Google Scholar 

  10. Nguyen, T.-K., Ahmad, Z., Kim, J.-M.: A scheme with acoustic emission hit removal for the remaining useful life prediction of concrete structures. Sensors 21(22), 7761 (2021). https://doi.org/10.3390/s21227761

    Article  Google Scholar 

  11. Li, J., Zi, Y., Wang, Y., Yang, Y.: Health indicator construction method of bearings based on Wasserstein dual-domain adversarial networks under normal data only. IEEE Trans. Ind. Electron. 69(10), 10615–10624 (2022). https://doi.org/10.1109/TIE.2022.3156148

    Article  Google Scholar 

  12. González-Muñiz, A., Díaz, I., Cuadrado, A.A., García-Pérez, D.: Health indicator for machine condition monitoring built in the latent space of a deep autoencoder. Reliab. Eng. Syst. Saf. 224, 108482 (2022). https://doi.org/10.1016/j.ress.2022.108482

    Article  Google Scholar 

  13. Wen, P., Zhao, S., Chen, S., Li, Y.: A generalized remaining useful life prediction method for complex systems based on composite health indicator. Reliab. Eng. Syst. Saf. 205, 107241 (2021). https://doi.org/10.1016/j.ress.2020.107241

    Article  Google Scholar 

  14. Shukla, S., Yadav, R.N., Sharma, J., Khare, S.: Analysis of statistical features for fault detection in ball bearing. In: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–7 (2015). https://doi.org/10.1109/ICCIC.2015.7435755

  15. Mahamad, A.K., Hiyama, T.: Development of artificial neural network based fault diagnosis of induction motor dearing. In: 2008 IEEE 2nd International Power and Energy Conference, pp. 1387–1392 (2008). https://doi.org/10.1109/PECON.2008.4762695

  16. Yu, J.: Local and nonlocal preserving projection for bearing defect classification and performance assessment. IEEE Trans. Ind. Electron. 59(5), 2363–2376 (2012). https://doi.org/10.1109/TIE.2011.2167893

    Article  Google Scholar 

  17. Leite, V.C.M.N., et al.: Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Trans. Ind. Electron. 62(3), 1855–1865 (2015). https://doi.org/10.1109/TIE.2014.2345330

    Article  Google Scholar 

  18. Cui, J., et al.: An improved wavelet transform and multi-block forecast engine based on a novel training mechanism. ISA Trans. 84, 142–153 (2019). https://doi.org/10.1016/j.isatra.2018.09.023

    Article  Google Scholar 

  19. **a, M., Li, T., Shu, T., Wan, J., De Silva, C.W., Wang, Z.: A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Trans. Ind. Informatics 15(6), 3703–3711 (2019). https://doi.org/10.1109/TII.2018.2868687

    Article  Google Scholar 

  20. Elforjani, M., Shanbr, S.: Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Trans. Ind. Electron. 65(7), 5864–5871 (2018). https://doi.org/10.1109/TIE.2017.2767551

    Article  Google Scholar 

  21. Liu, K., Gebraeel, N.Z., Shi, J.: A data-level fusion model for develo** composite health indices for degradation modeling and prognostic analysis. IEEE Trans. Autom. Sci. Eng. 10(3), 652–664 (2013). https://doi.org/10.1109/TASE.2013.2250282

    Article  Google Scholar 

  22. Mosallam, A., Medjaher, K., Zerhouni, N.: Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J. Intell. Manuf. 27(5), 1037–1048 (2014). https://doi.org/10.1007/s10845-014-0933-4

    Article  Google Scholar 

  23. Nguyen, K.T.P., Medjaher, K.: An automated health indicator construction methodology for prognostics based on multi-criteria optimization. ISA Trans. 113, 81–96 (2021). https://doi.org/10.1016/j.isatra.2020.03.017

    Article  Google Scholar 

  24. Liao, L.: Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans. Ind. Electron. 61(5), 2464–2472 (2014). https://doi.org/10.1109/TIE.2013.2270212

    Article  Google Scholar 

  25. Han, T., Liu, C., Yang, W., Jiang, D.: Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. ISA Trans. 93, 341–353 (2019). https://doi.org/10.1016/j.isatra.2019.03.017

    Article  Google Scholar 

  26. Gugulothu, N., Vishnu, T.R., Malhotra, P., Vig, L., Agarwal, P., Shroff, G.M.: Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks. Ar**v, vol. abs/1709.0 (2017)

    Google Scholar 

  27. Bektas, O., Jones, J.A., Sankararaman, S., Roychoudhury, I., Goebel, K.: A neural network filtering approach for similarity-based remaining useful life estimation. Int. J. Adv. Manuf. Technol. 101(1–4), 87–103 (2018). https://doi.org/10.1007/s00170-018-2874-0

    Article  Google Scholar 

  28. Miller, R.K., Hill, E.K., Moore, P.O.: Third Edition Technical Editors

    Google Scholar 

  29. Rodríguez, P., Celestino, T.B.: Application of acoustic emission monitoring and signal analysis to the qualitative and quantitative characterization of the fracturing process in rocks. Eng. Fract. Mech. 210, 54–69 (2019). https://doi.org/10.1016/j.engfracmech.2018.06.027

    Article  Google Scholar 

  30. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004). https://doi.org/10.1023/B:MACH.0000008084.60811.49

    Article  MATH  Google Scholar 

  31. Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951). https://doi.org/10.1214/aoms/1177729694

    Article  MathSciNet  MATH  Google Scholar 

  32. M.G.K. undefined: Theory of probability. By Harold Jeffreys. Second Edition. Pp. vii, 411. 30s. 1948. (Oxford University Press). Math. Gaz. 32(302), 304 (1948). https://doi.org/10.2307/3609901

  33. Tra, V., Nguyen, T.K., Kim, C.H., Kim, J.M.: Health indicators construction and remaining useful life estimation for concrete structures using deep neural networks. Appl. Sci. 11(9), 4113 (2021). https://doi.org/10.3390/app11094113

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Korea Technology and Information Promotion Agency (TIPA) grant funded by the Korea government (SMEs) (No. S3126818). This work was also supported by the Technology Infrastructure Program funded by the Ministry of SMEs and Startups (MSS, Korea).

Author information

Authors and Affiliations

  1. Department of Electrical, Electronics, and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea

    Tuan-Khai Nguyen, Zahoor Ahmad & Jong-myon Kim

Authors
  1. Tuan-Khai Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zahoor Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jong-myon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jong-myon Kim .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Seoul, Korea (Republic of)

    Tai M. Chung

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nguyen, TK., Ahmad, Z., Kim, Jm. (2022). Comparison of Health Indicators Construction for Concrete Structure Using Acoustic Emission Hit and Kullback-Leibler Divergence. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_41

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8069-5_41

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8068-8

  • Online ISBN: 978-981-19-8069-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation