Fault-Tolerant Weld Line Detection Using Image Processing and Fusion of Execution Monitoring Systems

  • Lucas Molina
  • Elyson Ádan Nunes Carvalho
  • Eduardo Oliveira Freire
  • Raimundo Carlos Silvério Freire


Quality control, cost reduction and above all, human and environmental safety are great reasons that stimulate the investments in technologies like automatic inspection. The automatic inspection of weld lines in storage tanks is of special interest, due to the fact that such tanks are currently used to store harmful products. For a reliable inspection it is necessary to accurately detect the weld line relative position and orientation with respect to the inspection robot. In this paper, the development of a system to perform weld line detection and estimation in storage tanks is proposed. The system uses visual information to perform the detection task and a fault-tolerant estimation process, main contribution of this work, to increase the confidence and performance of the detection system. The fault-tolerant estimation is based on the \(\alpha \)\(\beta \) filter and on a knowledge-based execution monitoring system, constructed through the fusion of two subsystems: a data-based and a model-based fault detection systems.


Weld line detection Fault-tolerant estimation  Image processing \(\alpha \)\(\beta \) filter 



The authors wish to thank CNPq and CAPES for the financial support that made possible the realization of this project.


  1. Carvalho, E. A. N. (2007). Sistema para posicionamento de sensores aplicado à inspeção automatizada de cordões de solda em tanques de armazenamento de combustíveis derivados do petróleo. Master’s thesis, PPGEE-UFCG, Campina Grande, PB. Google Scholar
  2. Carvalho, E. A. N., Molina, L., Freire, E. O., Freire, R. C. S., & Luciano, B. (2007). IEEE-IMTC: Fillet weld identification for automatic inspection of spherical tanks.Google Scholar
  3. Carvalho, E. A. N., Molina, L., Freire, E. O., Luciano, B., & Freire, R. C. S. (2008). Sistema para identificação, localização e estimação de cordões de solda presentes em tanques de armazenamento de combustíveis derivados do petróleo. XVII CBA.Google Scholar
  4. Cassandras, C. G. & Lafortune, S. (2006). Introduction to discrete event systems. New York: Springer.Google Scholar
  5. Deutsch, W. A. K. (2000). Automated ultrasonic inspection—examples from the steel mill. World conference for nondestructive testing.Google Scholar
  6. Deutsch, W. A. K., Schulte, P., Joswig, M. & Kattwinkel, R. (2006). Automatic inspection of welded pipes with ultrasound, 9th ECNDT.Google Scholar
  7. Duda, R. O., & Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures. ACM Communication, 15, 11–15.CrossRefGoogle Scholar
  8. Gertler, J. J. (1998). Fault detection and diagnosis in engineering systems (Vol. I). CRC.Google Scholar
  9. Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing. Upper Saddle: Prantice Hall.Google Scholar
  10. Greig, A., & Broome, D. (1991). Automatic of complex geometry welds, 5th IEEE international conference on advanced robotics.Google Scholar
  11. Horn, B. K. P. (1986). Robot visiong. New York: McGraw-Hil.Google Scholar
  12. Hough, P. V. C. (1962). Method and means for recognizing complex patterns. Us Patent 3,069,654.Google Scholar
  13. Kalata, P. R., & Murphy, K. M. (1997). Alpha-beta target tracking and track rate variations, 29th southeastern symposium on system theory.Google Scholar
  14. Kapur, J. N., Sahoo, P. K., & Wong, K. C. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 29, 273–285.CrossRefGoogle Scholar
  15. Kälviäinen, H., Hirvonen, P., Xu, L., & Oja, E. (1995). Probabilistic and non-probabilistic hough transforms: Overview and comparisons. Image and Vision Computing, 13(4), 239–252.Google Scholar
  16. Molina, L., Carvalho, E. A. N., Freire, E. O., Montalvão, J. R., & Chagas, F. A. (2008). IEEE-LARS: A robotic vision system using a modified hough transform to perform weld line detection on storage tanks.Google Scholar
  17. Molina, L., Carvalho, E. A. N., Moura, M. A., Freire, E. O., & Montalvão, J. R. (2008b). Um métodos de visão robótica para identificação de cordães de solda em tanques de armazenamento visando inspeção automatizada. XVII CBA.Google Scholar
  18. Molina, L., Freire, E. O., Carvalho, E. A. N., & Basilio, J. C. S. (2009). Fault-tolerant weld line detection for automatic inspection of storage tanks based on visual information and alpha-beta filter. IX SBAI.Google Scholar
  19. Noriega, G., & Pasupathy, S. (1992). Application of kalman filtering to real-time preprocessing of geophysical data. IEEE Transactions on Geoscience and Remote Sensing, 30(5), 897–910.CrossRefGoogle Scholar
  20. Oppenheim, A. V., & Schafer, R. W. (1989). Discrete-time signal processing. Toronto: Prentice-Hall Inc.zbMATHGoogle Scholar
  21. Pettersson, O. (2005). Execution monitoring in robotics: A survey. Robotics and Autonomous Systems, 53, 73–88.CrossRefGoogle Scholar
  22. Platte, M., Deutsch, V., Vogt, M., & Deutsch, W. A. K. (2002). Ultrasonic testing—compact and understandable. Wuppertal: Castell.Google Scholar
  23. Schanze, T. (1995). Sinc interpolation of discrete periodic signals. IEEE Transactions on Signal Processing, 43(6), 1502–1503.CrossRefGoogle Scholar
  24. Tsuge, H. (1988). Automation of in-service inspection of spherical tanks. Weld. Int., 2(7), 649–652.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2013

Authors and Affiliations

  • Lucas Molina
    • 1
    • 2
  • Elyson Ádan Nunes Carvalho
    • 2
  • Eduardo Oliveira Freire
    • 2
  • Raimundo Carlos Silvério Freire
    • 1
  1. 1.Electrical Engineering DepartmentFederal University of Campina Grande–DEE/UFCGCampina GrandeBrazil
  2. 2.Electrical Engineering DepartmentFederal University of Sergipe–DEL/UFSSão CristóvãoBrazil

Personalised recommendations