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Welding fault detection and diagnosis using one-class SVM with distance substitution kernels and random convolutional kernel transform

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Abstract

Welding defect detection in the manufacturing of hot water tanks is still often performed by human visual inspection or with the help of classical non-destructive tests such as liquid penetrant. These quality inspection methods can be time-consuming and have an important error rate. As a potential alternative, machine learning algorithms can be employed to automate fault detection in this process. In this paper, we propose an approach for the detection of welding faults by identifying abnormal subsequences of the welding voltage signal. The novelty of our approach is in employing the one-class SVM with distance substitution kernels, which, unlike previous works employing the one-class SVM, allows us to work directly with the raw subsequences. This permits easier generalization of the detection across different types of signals and achieves higher detection accuracy. The results show that the approach is both accurate and fast compared to existing approaches, which makes it more suitable for real-time welding monitoring. We further propose an approach for the automatic diagnosis of welding defects using the random convolutional kernel transform.

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Funding

Funding for the project was provided by elm.leblanc and the Association Nationale de la Recherche et de la Technologie (ANRT).

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Authors and Affiliations

  1. Mines Saint-Etienne, Univ Clermont Auvergne, CNRS, UMR 6158 LIMOS, Institut Henri Fayol, Saint-Etienne, 42023, France

    Abdallah Amine Melakhsou & Mireille Batton-Hubert

  2. elm.leblanc, 6 Rue des Écoles, Saint-Thégonnec Loc-Eguiner, 29410, France

    Abdallah Amine Melakhsou & Nicolas Casoetto

Authors
  1. Abdallah Amine Melakhsou
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  2. Mireille Batton-Hubert
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  3. Nicolas Casoetto
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Contributions

Abdallah Amine Melakhsou: data collection, formal analysis, investigation, methodology, experimentation, and writing. Mireille Batton-Hubert: methodology, review, and supervision. Nicolas Casoetto: review and supervision.

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Correspondence to Abdallah Amine Melakhsou.

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Melakhsou, A.A., Batton-Hubert, M. & Casoetto, N. Welding fault detection and diagnosis using one-class SVM with distance substitution kernels and random convolutional kernel transform. Int J Adv Manuf Technol 128, 459–477 (2023). https://doi.org/10.1007/s00170-023-11768-5

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  • DOI: https://doi.org/10.1007/s00170-023-11768-5

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