By Xavier Gros DUT BSc(Hon) MSc PhD
Facts fusion is a speedily constructing know-how which contains the combo of knowledge provided by means of a number of NDT (Non-Destructive checking out) sensors to supply a extra whole and comprehensible photo of structural integrity. this article is the 1st to be committed completely to the concept that of multisensor integration and information fusion utilized to NDT. the benefits of this system are largely stated and the writer provides a very good advent to facts fusion approaches. difficulties are approached gradually via precise case reviews, delivering useful counsel for these wishing to enhance and discover NDT facts fusion further.
This e-book will turn out useful to inspectors, scholars and researchers fascinated by NDT sign processing measurements and trying out. It exhibits the nice price and significant merits that are completed by means of imposing multisensor information fusion, not just in NDT but additionally in any self-discipline the place measurements and trying out are key actions.
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Extra resources for NDT Data Fusion
A Pseudo k-means approach to the multisensor multitarget tracking problem, Proceedings of the SPIE, 1470, Data Structures and Target Classification, April 1991, Orlando, USA, 48-58. 42 Data fusion - a review 98. Gerhart G, Martin G, Gonda T. Thermal Image Modeling, Proceedings of the SPIE, 782, Infrared Sensors and Sensor Fusion, May 1987, Orlando, Florida, USA, 3-9. 99. Macnicol G. Complex Imagery, Sept. 1991, Computer Graphics World, 75-9. 100. Henderson T, Weitz E, Hansen C, Mitiche A. Multisensor knowledge systems: Interpreting 3-D structure, Dec.
8a Centralised signal detection system Distributed signal detection systems fused identity declarations using Bayesian theory, the Dempster-Shafer paradigm or Thomopoulos generalised evidence processing (GEP). The output from each sensor is a decision and these decisions form the inputs to a fusion centre where association is performed. Centralised systems are more suitable for the fusion of raw data but the association phase can be difficult. They are used with commensurate 16 Data fusion - a review Sensor 1 - • Measurement - Local Decision Feature Sensor 2 -•Measurement Extraction Local Decision Level Sensor N - • Measurement - Local Decision Fig.
The limitations and advantages of the most common NDT methods suited to data fusion are reviewed in this chapter. The sensitivity and accuracy of each method is dependent upon its application, and the performance of NDT systems can be compared and assessed using probabilistic and statistical analysis. Such studies are becoming more common in industry in order to define the best suited method or defect detection technique for a specific application. These performance criteria will be discussed in this chapter.
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