Multisensor Data Fusion and Integration in NDT

In a previous publication the concept of data fusion applied to non-destructive testing (NDT) was introduced [1]. The present book explores the concept of NDT data fusion through a comprehensive review and analysis of current applications. Its main objective is to provide the NDT community with an up-to-date publication containing writings by authoritative researchers in the field of data fusion. It is not intended to give rigorous scientific details, but more a pragmatic overview of several applications of data fusion for materials evaluation and condition monitoring.

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References

  1. Gros X.E., NDT Data Fusion, Butterworth-Heinemann, 1997. Google Scholar
  2. Dasarathy B.V., Sensor fusion potential exploitation — Innovative architectures and illustrative applications, Proc. IEEE, 1997, 85(1):24–38. ArticleGoogle Scholar
  3. Hall D.L., Llinas J., An introduction to multisensor data fusion, Proc. IEEE, 1997,85(l):6–23. ArticleGoogle Scholar
  4. Spedding V., Computer integrate disparate data, Scientific Computing World, 1998, (38):22–23. Google Scholar
  5. Georgel B., Lavayssière B., Fusion de données: un nouveau concept en CND, Proc. 6 th European Conf. on NDT, 1994, Nice, France, 1:31–35. Google Scholar
  6. Gros X.E. Strachan P., Lowden D.W., Edwards I., NDT data fusion, Proc. 6 th European Conf. on NDT, 1994, Nice, France, 1:355–359. Google Scholar
  7. Dessendre M., Thevenot F., Liot A., Tretout H., Exmelin A., NDT and CAD data fusion for diagnosis assistance and diffusion bonding improvement, Proc. COFREND Congress on NDT, 1997, Nantes, France. Google Scholar
  8. Valet L., Mauris G., Bolon P., A statistical overview of recent literature in information fusion, Proc. 3 rd Inter. Conf. on Information Fusion, 2000, Paris, France. Google Scholar
  9. Nelson J.M., Cruikshank D., Galt S., A flexible methodology for analysis of multiple-modality NDE data, Proc. Review of Progress in QNDE, 1989, LaJoila, USA, 8:819–826. Google Scholar
  10. Janney D.H., Image processing in nondestructive testing, Proc. 23 rd Sagamore Army Materials Research Conf. on the Nondestructive Characterization of Materials, 1979, 409–420. Google Scholar
  11. Troll tunnel inspection ROV piloted in virtual reality mode. Offshore, 1996, 56:141–142. Google Scholar
  12. Gros X.E., Strachan P., Lowden D., Fusion of multiprobe NDT data for ROV inspection, Proc. MIS/IEEE Conf. Oceans’95,1995, San Diego, USA, 3:2046–2050. Google Scholar
  13. Gianaris NJ., Green R.E., Statistical methods in material manufacturing and evaluation, Materials Evaluation, 1999, 57(9):944–951. Google Scholar
  14. Lallier E., Farooq M., A real time pixel-level based image fusion via adaptive weight averaging, Proc. 3 rd Inter. Conf. on Information Fusion, 2000, Paris, France. Google Scholar
  15. Petrović V., Xydeas C, Computationally efficient pixel-level image fusion, Proc. 3 rd Inter. Conf. on Information Fusion, 2000, Paris, France. Google Scholar
  16. Fu P., Hope A.D., Javed M.A., Fuzzy classification of milling tool wear, Insight, 1997, 39(8):553–557. Google Scholar
  17. Goebel K., Badami V., Perera A., Diagnostic information fusion for manufacturing processes, Proc. 2 nd Inter. Conf. on Information Fusion, FUSION'99,1999, Sunnyvale, USA, 1:331–336. Google Scholar
  18. Liu Z., Gros X.E., Tsukada K., Hanasaki K., 3D visualization of ultrasonic inspection data by using AVS, Proc. 5 th Far-East Conf. on NDT, 1999, Renting, Taiwain, 549–554. Google Scholar
  19. Pastorino M., Inverse-scattering techniques for image reconstruction, IEEE Instrumentation & Measurement Magazine, 1998, 1(4):20–25. ArticleGoogle Scholar
  20. Summa D.A., Claytor T.N., Jones M.H., Schwab M.J., Hoyt S.C., 3-D Visualisation of x-ray and neutron computed tomography (CT) and full waveform ultrasonic (UT) data, Proc. Review of Progress in QNDE, 1999, Snowbird, USA, 18A:927–934. Google Scholar
  21. Vengrinovich V.V., Denkevich Y.B., Tillack G.R., Jacobsen C., 3D x-ray reconstruction from strongly incomplete noisy data, Proc. Review of Progress in QNDE, 1999, Snowbird, USA, 18A:935–942. Google Scholar
  22. Reed J.M., Hutchinson S., Image fusion and subpixel parameter estimation for automated optical inspection of electronic component, IEEE Trans, on Industrial Electronics, 1996, 43(3):346–354. ArticleGoogle Scholar
  23. Bossi R.H., Nelson J., NDE Data Fusion, Proc. ASNT Fall Conf., 1997, Pittsburgh, USA. Google Scholar
  24. Matuszewski B.J., Shark L.K., Varley M.R., Smith J.P., Region-based wavelet fusion of ultrasonic, radiographic and shearographic non-destructive testing images, Proc. 15 th World Conf. on NDT, 2000, Rome, Italy, paper N°263. Google Scholar
  25. Siegel M., Gunatilake P., Enhanced remote visual inspection of aircraft skin, Proc. Workshop on Intelligent NDE Sciences for Ageing and Futuristic Aircraft, 1997, El Paso, USA, 101–112. Google Scholar
  26. Paul French, Biomedical optics, Physics World, 1999, 12(6):41–46. ArticleGoogle Scholar
  27. Ault T., Siegel M.W., Frameless patient registration using ultrasonic imaging, J. of Image Guided Surgery, 1995, 1(2):94–102. ArticleCASGoogle Scholar
  28. Strangman G., Under doctors’ orders for a digital revolution, Scientific Computing World, 2000, (52):27–30. Google Scholar
  29. Zachary J.M., Iyengar S.S., Three dimensional data fusion for biomedical surface reconstruction, Proc. 7 th Fusion Conf., 1999, Sunnyvale, USA. Google Scholar
  30. Gautier L., Taleb-Ahmed A., Rombaut M., Postaire J.-G., Leclet H., Belief function in low level data fusion: application in MRI images of vertebra, Proc. 3 rd Inter. Conf. on Information Fusion, 2000, Paris, France. Google Scholar
  31. Mangin J.F., Stévenet J.F., Le cerveau s’affiche en dynamique et en haute définition, CEA Technologies, 2000, (49):2. Google Scholar
  32. Dupuis O., Fusion entre les données ultrasonores et les images de radioscopie à haute Résolution: application au contrôle de cordon de soudure, PhD Thesis, 2000, INSA Lyon, France. Google Scholar
  33. Dupuis O., Kaftandjian V., Drake S., Hansen A., Ffreshex: a combined system for ultrasonic and X-ray inspection of welds, Proc. 15 th World Conf. on NDT, 2000, Rome, Italy, paper N°286. Google Scholar
  34. Just V., Fleuet E., Gautier S., The BE-MISTRAL project: a fully multi-technique approach from acquisition to data fusion, Proc. 7 th ECNDT, 1998, Copenhagen, Denmark 2:1608–1613. Google Scholar
  35. Jain A.K., Dubuisson M.P., Madhukar M.S., Multi-sensor fusion for nondestructive inspection of fiber reinforced composite materials, Proc. 6 th Tech. Conf. of the American Society for Composites, 1991, 941–950. Google Scholar
  36. Yanowitz S.D., Bruckstein A.M., A new method for image segmentation, Computer Vision, Graphics and Image Processing, 1989, 46(4):82–95. ArticleGoogle Scholar
  37. Barniv Y., Casasent D., Multisensor image registration: experimental verification, Proc. SPIE, Process. Images and Data from Optical Sensors, 1981, San Diego, USA, 292:160–171. Google Scholar
  38. Akerman A., Pyramidal techniques for multisensor fusion, Proc. SPIE, Sensor Fusion V, 1992, Boston, USA, 1828:124–131. Google Scholar
  39. Haberstroh R., Kadar I., Multi-spectral data fusion using neural networks, Proc. SPIE, Signal processing, sensor fusion, and target recognition H, 1993, Orlando, USA, 1955:65–75. Google Scholar
  40. L. Ghouti, A novel method for automatic defect classification using artificial neural networks, Proc. Review of Progress in QNDE, 1999, Snowbird, USA, 18A:843–850. Google Scholar
  41. Wang C, Cannon D.J., Virtual-reality-based point-and-direct robotic inspection in manufacturing, IEEE Trans, on Robotics and Automation, 1996, 12(4):516–531. ArticleGoogle Scholar
  42. Hannah P., Starr A., Ball A., Decisions in condition monitoring — an example for data fusion architecture, Proc. 3 rd Inter. Conf. on Information Fusion, 2000, Paris, France. Google Scholar
  43. Stover J.A., Hall D.L., Gibson R.E., A fuzzy-logic architecture for autonomous multisensor data Jusion, IEEE Trans, on Industrial Electronics, 1996, 43(3):403–410. ArticleGoogle Scholar
  44. Russo F., Recent advances in fuzzy techniques for image enhancement, IEEE Instrumentation & Measurement Magazine, 1998, 1(4):29–32. ArticleGoogle Scholar
  45. Benoist Ph., Besnard R., Bayon G., Boutaine JL., CIVA poste d’expertise en controle non destructif, Proc. 6 th Europ. Conf. on NDT, 1994, Nice, France, 2:1311–1315. Google Scholar
  46. Benoist P., Besnard R., Bayon G., Boutaine J.L., CIVA Workstation for NDE: Mixing of NDE Techniques and Modeling, Review of Progress in QNDE, Plenum Press, 1995, Snowmass Village, USA, 14B:2353–2360. Google Scholar
  47. Nockemann C, Heine S., Johannsen K., Schumm A., Vailhen O., Nouailhas B., Raising the reliability of NDE by combination and standardisation ofNDT-data using the Trappist system, Proc. Review of Progress in QNDE, 1996, Seattle, USA, 15A:1975–1982. Google Scholar
  48. Just V., Gros P.O., Soors C, Francois D., PACE a comprehensive multitechnique analysis system applied to the analysis of bottom head penetration tubes NDT data, Proc. 7 th Euro. Conf. on NDT, 1998, Copenhagen, Denmark, 2:1442–1447. Google Scholar
  49. Dargahi J., Payandeh S., Surface texture measurement by combining signals from two sensing elements of a piecoelectric tactile sensor, Proc. SPIE AeroSense Conf., 1998, Orlando, USA, 3376:122–128. Google Scholar
  50. Taylor O., Mayintyre J., Adaptive local fusion systems for novelty detection and diagnostics in condition monitoring, Proc. SPIE AeroSense Conf., 1998, Orlando, USA, 3376:210–218. Google Scholar
  51. Mou J., Jones S.D., Furness RJ., Sensor-fusion methodology for reducing product quality variation, Proc. Computer Applications in Production and Engineering, 1997, London, U.K., 398–410. Google Scholar
  52. Liu Z., Studies on data fusion of nondestructive testing, PhD Thesis, 2000, Kyoto University. Google Scholar
  53. Hayashi K., Yamaji H., Nagata Y., Ishida T., Combined method of high resolution x-ray computed tomography and acoustic emission for nondestructive testing, Proc. Inter. Symp. on NDT & Stress-Strain Measurement FENDT, 1992, Tokyo, Japan, 361–366. Google Scholar
  54. Rakocevic M., Wang X., Chen S., Khalid A., Sattar T., Bridge B., Development of an automated mobile robot vehicle inspection system for NDT of large steel plates, Insight, 1999, 41(6):376–382. Google Scholar
  55. Tewari A., Gokhale A.M., Application of three-dimensional digital image processing for reconstruction of microstructural volume from serial sections, Materials Characterization, 2000, 44(3):259–269. ArticleCASGoogle Scholar
  56. Fink M., Prada C., Ultrasonic focusing with time reversal mirrors, Advances in Acoustic Microscopy, Eds. A. Briggs, W. Arnold, Plenum Press, New York, USA, 1996, 2:219–251. ChapterGoogle Scholar

Author information

Authors and Affiliations

  1. Independent NDT Centre, Bruges, France Xavier E. Gros
  1. Xavier E. Gros