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Damage Detection in Rectangular Laminated Composite Plate Structures using a Combination of Wavelet Transforms and Artificial Neural Networks

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Abstract

Purpose

Although damage's location detection by Wavelet Transforms (WTs) is quantitive, damage's level detection is qualitative. It is a weakness of wavelet transform that cannot detect the level of damages quantitatively. For overcoming this weakness, this paper proposes a novel robust approach for quantifying the damage level of rectangular laminated composite plates using a combination of wavelet transforms and Artificial Neural Networks (WT-ANN).

Methods

The finite element method generates two-dimensional signals of vibration amplitudes in the rectangular laminated composite plates. Next, a two-dimensional WTs is applied to the signals to generate wavelet coefficients and wavelet modulus. An artificial neural network is developed to quantify damage's level based on damage locations detected by wavelet transform and fundamental natural frequency.

Results

Findings demonstrate that it is possible to quantify damages in rectangular laminated composite plates with high accuracy (R = 0.992). A disturbing phenomenon in damage identification techniques based on wavelet transform is discovered and called “edge noise”.

Conclusion

Results show that the proposed WT-ANN technique can detect all single-damage scenarios with very high accuracy by eliminating edge noises.

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Acknowledgements

The first three authors acknowledge the funding support of Babol Noshirvani University of Technology through Grant program No. BNUT/965919012/99.

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Authors and Affiliations

  1. Babol Noshirvani University of Technology, Mazandaran Province, Babol, Iran

    Morteza Saadatmorad, Ramazan-Ali Jafari-Talookolaei & Mohammad-Hadi Pashaei

  2. Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam

    Samir Khatir

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  1. Morteza Saadatmorad
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  2. Ramazan-Ali Jafari-Talookolaei
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Correspondence to Ramazan-Ali Jafari-Talookolaei.

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Saadatmorad, M., Jafari-Talookolaei, RA., Pashaei, MH. et al. Damage Detection in Rectangular Laminated Composite Plate Structures using a Combination of Wavelet Transforms and Artificial Neural Networks. J. Vib. Eng. Technol. 10, 1647–1664 (2022). https://doi.org/10.1007/s42417-022-00471-6

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