Advertisement

Acoustic Emissions Detection and Ranging of Cracks in Metal Tanks Using Deep Learning

  • Gian Carlo Cardarilli
  • Luca Di NunzioEmail author
  • Rocco Fazzolari
  • Daniele Giardino
  • Marco Matta
  • Marco Re
  • Sergio Spanò
Conference paper
  • 11 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 627)

Abstract

This work proposes a new method for the estimation of the distance of cracks in pressure metal tanks. This method is obtained coupling the acoustic emissions analysis and the deep learning techniques. Using a 2D CNN we are able to estimate the distance between a crack and an acoustic emission piezoelectric sensor. The CNN is trained on images representing the spectrogram of acoustic emission located at distances of 2, 20, 40, 60, 80, 100, 120 and 140 cm. We obtained a RMSE of 2.54 cm.

References

  1. 1.
    Attuazione della direttiva 97/23/CE in materia di attrezzature a pressione. dlgs 93/2000 Italian LegislationGoogle Scholar
  2. 2.
    Cardarilli GC, Di Nunzio L, Massimi F, Fazzolari R, De Petris C, Augugliaro G, Mennuti C (2018) A wireless sensor node for acoustic emission non-destructive testing. Lect Notes Electr EngGoogle Scholar
  3. 3.
    Bechhoefer E, Qu Y, Zhu J, He D (2013) Signal processing techniques to improve an acoustic emissions sensor. In: Proceedings of the annual conference of the prognostics and health management society. pp 581–58Google Scholar
  4. 4.
    Grosse Christian U, Ohtsu M (eds) (2008) Acoustic emission testing. Springer Science & Business Media, BerlinGoogle Scholar
  5. 5.
    Akyildiz Ian F et al (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422CrossRefGoogle Scholar
  6. 6.
    Perumalla V, Ramanjaneyulu BS, Kolli A (2017) Simulation study of topological structures and node coordinations for deterministic WSN with TSCH. Int J Inform Vis 1(4)Google Scholar
  7. 7.
    Giardino D, Matta M, Spanò S (2019) A feature extractor IC for acoustic emission non-destructive testing. Int J Adv Sci Eng Inf Technol 9(2):538–543CrossRefGoogle Scholar
  8. 8.
    Giuliano R, Mazzenga F, Neri A, Vegni AM (2017) Security access protocols in IoT capillary networks. IEEE Internet Things J 4(3):645–657CrossRefGoogle Scholar
  9. 9.
    Riqualificazione serbatoi GPL con metodo EA, Istituto nazionale per l’assicurazione contro gli infortuni sul lavoro (INAIL) (2019). https://www.inail.it/cs/internet/attivita/ricerca-e-tecnologia/certificazione-verifica-e-innovazione/certificazione/riqualificazione-serbatoi-gpl-con-metodo-ea.html
  10. 10.
    Ni Q-Q, Iwamoto M (2002) Wavelet transform of acoustic emission signals in failure of model composites. Eng Fract Mech 69(6):717–728CrossRefGoogle Scholar
  11. 11.
    Lu Y (2017) Industry 4.0: A survey on technologies, applications and open research issues. J Ind Inf Integr 6:1–10Google Scholar
  12. 12.
    Matta M, Cardarilli GC, Di Nunzio L, Fazzolari R, Giardino D, Nannarelli A, Re M, Spanò S (2019) A reinforcement learning based QAM/PSK symbol synchronizer. IEEE AccessGoogle Scholar
  13. 13.
    Cardarilli GC, Di Nunzio L, Fazzolari R, Nannarelli A, Re M, Spano S (2019) N-dimensional approximation of euclidean distance. IEEE Trans Circuits Syst II Express BriefsGoogle Scholar
  14. 14.
    Cardarilli GC, Di Nunzio L, Fazzolari R, Re M, Spano S (2019) AW-SOM, an algorithm for high-speed learning in hardware self-organizing maps. IEEE Trans Circuits Syst II: Express BriefsGoogle Scholar
  15. 15.
    Cardarilli GC, Di Nunzio L, Fazzolari R, Giardino D, Matta M, Re M, Silvestri F, Spanò S (2019) Efficient ensemble machine learning implementation on FPGA using partial reconfiguration. Lect Notes Electr Eng 550:253–259CrossRefGoogle Scholar
  16. 16.
    Hordri NF, Yuhaniz SS, Shamsuddin SM (2016) Deep learning and its applications: a review. In: Postgraduate annual research on informatics seminar 2016, Universiti Teknologi MalaysiaGoogle Scholar
  17. 17.
    Russakovsky O et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252MathSciNetCrossRefGoogle Scholar
  18. 18.
    Zhu L, Peng Z, McClellan J (2018) Deep learning for seismic event detection of earthquake aftershocks. In: 2018 52nd asilomar conference on signals, systems, and computers, IEEEGoogle Scholar
  19. 19.
    Zhang J et al (2019) Fine-grained ECG classification based on deep CNN and online decision fusion. Preprint at arXiv:1901.06469
  20. 20.
    ASTM E-976, Standard Guide for Determining the Reproducibility of Acoustic Emission Sensor Response, ASTM InternationalGoogle Scholar
  21. 21.
    Burrascano P, Laureti S, Senni L, Ricci M (2018) Pulse compression in nondestructive testing applications: reduction of near sidelobes exploiting reactance transformation. IEEE Trans Circuits Syst I Regul Pap (99):1–11.  https://doi.org/10.1109/tcsi.2018.2862868

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gian Carlo Cardarilli
    • 1
  • Luca Di Nunzio
    • 1
    Email author
  • Rocco Fazzolari
    • 1
  • Daniele Giardino
    • 1
  • Marco Matta
    • 1
  • Marco Re
    • 1
  • Sergio Spanò
    • 1
  1. 1.Department of Electronic EngineeringUniversity of Rome Tor VergataRomeItaly

Personalised recommendations