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Welding monitoring and defect detection using probability density distribution and functional nonparametric kernel classifier

Author

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  • Abdallah Amine Melakhsou

    (Ecole Nationale Superieure des Mines de Saint-Etienne)

  • Mireille Batton-Hubert

    (Mines Saint-Etienne, University of Clermont Auvergne, CNRS, UMR 6158 LIMOS, Institut Henri Fayol)

Abstract

Welding fault detection in the industry of hot water tanks remains typically conducted visually or with the assistance of None Destructive Examination, such as X-ray, ultrasound, and penetrant testing. However, this leads to high consumption of time and resources. We propose in this paper a two-level method for automatic welding defect detection and localization. The method is based on the classification of the probability density distributions of the voltage signals underlying the generated stochastic process from the welding operation. In the main phase, we apply a passband filter to the raw signals and use the Kernel Density Estimation to measure the distribution of the filtered signal. The probability density distributions are processed as functional data and classified employing a functional non-parametric kernel classifier. In the second phase, the signal of nonconforming welding is split into segments and their probability density distributions are classified in order to extract the precise location of the defect in the whole signal. The proposed method allows to detect and localize welding defects with high accuracy.

Suggested Citation

  • Abdallah Amine Melakhsou & Mireille Batton-Hubert, 2023. "Welding monitoring and defect detection using probability density distribution and functional nonparametric kernel classifier," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1469-1481, March.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:3:d:10.1007_s10845-021-01871-3
    DOI: 10.1007/s10845-021-01871-3
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    References listed on IDEAS

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    1. Zhifen Zhang & Shanben Chen, 2017. "Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 207-218, January.
    2. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
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