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Convolutional neural network-based fracture detection in spectrogram of acoustic emission

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Abstract

Acoustic emission (AE) has emerged as a pivotal non-destructive testing technique in structural health, particularly in high-strength steel and similar materials. Two-dimensional spectrograms exhibit unique challenges due to variations in both spatial and temporal, compounded by limited data availability. This research addresses the challenges in two primary objectives: first, the development of convolutional neural networks (CNNs) for fracture detection and, second, the investigation of data augmentation techniques to enhance the CNNs model’s training dataset, with a focus on the impact of various data augmented spectrograms. To achieve the first objective, AE signals undergo a conversion from their time-domain representation to spectrograms using the short-time Fourier transform. This transformation into a time–frequency format enables for the extraction of valuable features from the signals, which has the potential to significantly enhance the precision and efficiency of structural health monitoring by automating the detection of damage. The second objective involves studying the applicability of data augmentation techniques to expand the training dataset in which two axes of spectrogram have different physical parameters. In this context, it is observed that vertically flipped spectrograms have a more significant impact on prediction accuracy compared to horizontal flips and rotations. These results emphasize the significance of data augmentation strategies in training deep learning models for AE-based fracture detection. The outcomes of this study hold promise for enhancing the safety and reliability of structures constructed from high-strength steel, ultimately contributing to the field of structural health monitoring and maintenance.

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References

  1. Lv, S., Li, K., Chen, J., Li, X.: Corrosion of high-strength steel wires under tensile stress. Materials13, 1–16 (2020).https://doi.org/10.3390/ma13214790

    Article  Google Scholar 

  2. Khalifeh, A.: Stress corrosion cracking, pp. 864–901 (2010).https://doi.org/10.1016/B978-044452787-5.00035-4

  3. Pirskawetz, S.M., Schmidt, S.: Detection of wire breaks in prestressed concrete bridges by Acoustic Emission analysis. Dev. Built Environ.14, 100151 (2023).https://doi.org/10.1016/j.dibe.2023.100151

    Article  Google Scholar 

  4. Sikdar, S., Liu, D., Kundu, A.: Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel. Compos. Part B Eng.228, 109450 (2022).https://doi.org/10.1016/j.compositesb.2021.109450

    Article  Google Scholar 

  5. Sharma, S.: Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential. SICOT J. (2023).https://doi.org/10.1051/sicotj/2023018

    Article  Google Scholar 

  6. Abayomi-Alli, O.O., Damaševičius, R., Qazi, A., Adedoyin-Olowe, M., Misra, S.: Data augmentation and deep learning methods in sound classification: a systematic review. Electronics11, 3795 (2022).https://doi.org/10.3390/electronics11223795

    Article  Google Scholar 

  7. Chu, H.C., Zhang, Y.L., Chiang, H.C.: A CNN sound classification mechanism using data augmentation. Sensors23, 6972 (2023).https://doi.org/10.3390/s23156972

    Article  Google Scholar 

  8. Kim, G., Han, D.K., Ko, H.: Specmix : a mixed sample data augmentation method for training with time-frequency domain features. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 1, pp. 6–10 (2021).https://doi.org/10.21437/Interspeech.2021-103

  9. Mujaddidurrahman, A., Ernawan, F., Wibowo, A., Sarwoko, E.A., Sugiharto, A., Wahyudi, M.D.: Speech emotion recognition using 2D-CNN with data augmentation (2021)

  10. Zhou, G., Chen, Y., Chien, C.: On the analysis of data augmentation methods for spectral imaged based heart sound classification using convolutional neural networks. BMC Med. Inform. Decis. Mak.22, 226 (2022).https://doi.org/10.1186/s12911-022-01942-2

    Article  Google Scholar 

  11. Hao, X., Liu, L., Yang, R., Yin, L., Zhang, L., Li, X.: A review of data augmentation methods of remote sensing image target recognition. Remote Sens.15, 1–40 (2023).https://doi.org/10.3390/rs15030827

    Article  Google Scholar 

  12. Baba, T.: Time-frequency analysis using short time Fourier transform. Open Acoust. J.5, 32–38 (2012)

    Article  Google Scholar 

  13. Patterson, R.D., Robinson, K., Holdsworth, J., Mckeown, D., Zhang, C., Allerhand, M.: Complex sounds and auditory images. In: Auditory Physiology and Perception. CNBH (1992)

  14. Kon, M., Raphael, L.: Wavelet transforms and time-frequency signal analysis (2001)

  15. Debnath, L., Shah, F.A., Debnath, L., Shah, F.A.: The Wigner–Ville distribution and time-frequency signal analysis (2015)

  16. Daniel, I.M., Luo, J.J., Sifniotopoulos, C.G., Chun, H.J.: Acoustic emission monitoring of fatigue damage in metals. Nondestruct. Test. Eval.14, 71–87 (1998).https://doi.org/10.1080/10589759808953043

    Article  Google Scholar 

  17. Dunegan, H.L.: Modal analysis of acoustic emission signals. J. Acoust. Emiss.15, 53–61 (1997)

    Google Scholar 

  18. Carrino, S., Guerne, J., Dreyer, J., Ghorbel, H., Schorderet, A., Montavon, R.: Machining quality prediction using acoustic sensors and machine learning (2020).https://doi.org/10.3390/proceedings2020063031

  19. Pham, M.T., Kim, J.M., Kim, C.H.: Intelligent fault diagnosis method using acoustic emission signals for bearings under complex working conditions. Appl. Sci.10, 1–14 (2020).https://doi.org/10.3390/app10207068

    Article  Google Scholar 

  20. Xin, H., Cheng, L., Diender, R., Veljkovic, M.: Fracture acoustic emission signals identification of stay cables in bridge engineering application using deep transfer learning and wavelet analysis. Adv. Bridge Eng.1, 1–16 (2020).https://doi.org/10.1186/s43251-020-00006-7

    Article  Google Scholar 

  21. Arun Solanki, S.P.: Music instrument recognition using deep convolutional neural networks. Int. J. Inf. Technol.14, 1659–1668 (2022).https://doi.org/10.1007/s41870-019-00285-y

    Article  Google Scholar 

  22. Oh, S., Lee, J.Y., Kim, D.K.: The design of CNN architectures for optimal six basic emotion classification using multiple physiological signals. Sensors20, 1–17 (2020).https://doi.org/10.3390/s20030866

    Article  Google Scholar 

  23. Müller, R., Ritz, F., Illium, S., Linnhoff-Popien, C.: Acoustic anomaly detection for machine sounds based on image transfer learning. In: ICAART 2021—Proceedings of the 13th International Conference on Agents and Artificial Intelligence, vol. 2, pp. 49–56 (2021)

  24. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis.128, 336–359 (2020).https://doi.org/10.1007/s11263-019-01228-7

    Article  Google Scholar 

  25. Salamon, J., Bello, J.P.: Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process. Lett.24, 279–283 (2017).https://doi.org/10.1109/LSP.2017.2657381

    Article  Google Scholar 

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Acknowledgements

The authors wish to thank the Directors of CSIR-CECRI and NIT-T for their support and encouragement in carrying out this work. CSIR-CECRI Reference Number: CECRI/PESVC/Pubs./2021-164.

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Authors and Affiliations

  1. Corrosion and Materials Protection Division, CSIR-Central Electrochemical Research Institute, Karaikudi, Tamil Nadu, 630003, India

    R. Monika

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India

    S. Deivalakshmi

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  1. R. Monika

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  2. S. Deivalakshmi

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Contributions

RM helped in data collection, modelling, and methodology. SD worked in supervision and validation.

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Correspondence toS. Deivalakshmi.

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Monika, R., Deivalakshmi, S. Convolutional neural network-based fracture detection in spectrogram of acoustic emission.SIViP18, 4059–4074 (2024). https://doi.org/10.1007/s11760-024-03053-z

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