Industrial Imaging and NDT/NDE

Non-destructive testing and evaluation (NDT/NDE) is nowadays a very active field of research focused on the non-invasive inspection of components, devices, and complex structures for checking their structural and functional integrity. Despite the increasing popularity of such approaches, several challenges still need to be addressed from the methodological and practical viewpoint, especially if real-time processing and automatic anomaly understanding is required. In this context, innovative methodologies have been developed by the members of the ELEDIA Research Center network to enable the real-time accurate diagnostics of complex monitoring information. More specifically, the research areas developed within ELEDIA include the following 

  • Development of methodologies and algorithms for the non-destructive detection, localization, and characterization of anomalies, defects, and cracks in solid structures (e.g., concrete, structural materials, composite materials);

  • Study and implementation of innovative quality examination of conductive tubes in production lines, industrial components, and power plants

  • Artificial-intelligence based structural health monitoring techniques for real-time diagnostics and prognostics of critical interconnected infrastructures.

 

Related Project: SnATCH

For additional information contact us at contact@eledia.org

Read More

  • M. Salucci, N. Anselmi, G. Oliveri, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "Real-time NDT-NDE through an innovative adaptive partial least squares SVR inversion approach," IEEE Transactions on Geoscience and Remote Sensing vol. 54, no. 11, pp. 6818-6832, November 2016 (DOI: 10.1109/TGRS.2016.2591439)
  • S. Ahmed, R. Miorelli, M. Salucci, and A. Massa, "Real-time flaw characterization through learning-by-examples techniques: a comparative study applied to ECT," Studies in Applied Electromagnetics and Mechanics - Electromagnetic Nondestructive Evaluation (XX) vol. 42, pp. 228-235, June 2017 (DOI: 10.3233/978-1-61499-767-2-228)
  • S. Ahmed, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "Advanced statistical learning method for multi-physics NDT-NDE," Journal of Physics: Conference Series vol. 1131, pp. 1-7 2018 (DOI: 10.1088/1742-6596/1131/1/012012)
  • M. Salucci, N. Anselmi, G. Oliveri, P. Rocca, S. Ahmed, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "A nonlinear kernel-based adaptive learning-by-examples method for robust NDE-NDT of conductive tubes,” Journal of Electromagnetic Waves and Applications, vol. 33, no. 6, pp. 669–696, February 2019 (DOI: 10.1080/09205071.2019.1572546).," Journal of Electromagnetic Waves and Applications vol. 33, no. 6, pp. 669–696, February 2019 (DOI: 10.1080/09205071.2019.1572546)
  • S. Ahmed, R. Miorelli, C. Reboud, P. Calmon, N. Anselmi, and M. Salucci, "Fast characterization of multiple cracks in conductive media based on adaptive feature extraction and SVR," Studies in Applied Electromagnetics and Mechanics - Electromagnetic Nondestructive Evaluation (XXI) vol. 24, no. 2, pp. 191-198 2018 (DOI: 10.3233/978-1-61499-836-5-191)