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Characterization of welding defects by fractal analysis of ultrasonic signals

Author

Listed:
  • Vieira, A.P.
  • de Moura, E.P.
  • Gonçalves, L.L.
  • Rebello, J.M.A.

Abstract

In this work we apply tools developed for the study of fractal properties of time series to the problem of classifying defects in welding joints probed by ultrasonic techniques. We employ the fractal tools in a preprocessing step, producing curves with a considerably smaller number of points than in the original signals. These curves are then used in the classification step, which is realized by applying an extension of the Karhunen–Loève linear transformation. We show that our approach leads to small error rates, comparable with those obtained by using more time-consuming methods based on non-linear classifiers.

Suggested Citation

  • Vieira, A.P. & de Moura, E.P. & Gonçalves, L.L. & Rebello, J.M.A., 2008. "Characterization of welding defects by fractal analysis of ultrasonic signals," Chaos, Solitons & Fractals, Elsevier, vol. 38(3), pages 748-754.
  • Handle: RePEc:eee:chsofr:v:38:y:2008:i:3:p:748-754
    DOI: 10.1016/j.chaos.2007.01.012
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    Cited by:

    1. de Moura, Elineudo Pinho & de Abreu Melo Junior, Francisco Erivan & Rocha Damasceno, Filipe Francisco & Campos Figueiredo, Luis Câmara & de Andrade, Carla Freitas & de Almeida, Maurício Soares & Alexa, 2016. "Classification of imbalance levels in a scaled wind turbine through detrended fluctuation analysis of vibration signals," Renewable Energy, Elsevier, vol. 96(PA), pages 993-1002.
    2. Masoud Vejdannik & Ali Sadr, 2018. "Automatic microstructural characterization and classification using probabilistic neural network on ultrasound signals," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1923-1940, December.
    3. Melo Junior, Francisco Erivan de Abreu & de Moura, Elineudo Pinho & Costa Rocha, Paulo Alexandre & de Andrade, Carla Freitas, 2019. "Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques," Energy, Elsevier, vol. 171(C), pages 556-565.

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