Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation

Paul D Wilcox*, Anthony J Croxford, Nicolas Budyn, Rhodri L T Bevan, Jie Zhang, Artem Kashubin, Peter Cawley

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Abstract

State-of-the-art ultrasonic Non-Destructive Evaluation (NDE) uses an array to rapidly generate multiple, information-rich views at each test position on a safety-critical component. However, the information for detecting potential defects is dispersed across views, and a typical inspection may involve thousands of test positions. Interpretation requires painstaking analysis by a skilled operator. In this paper various methods for fusing multi-view data are developed. Compared to any one single view, all methods are shown to yield significant performance gains, which may be related to the general and edge cases for NDE. In the general case, a defect is clearly detectable in at least one individual view, but the view(s) depends on the defect location and orientation. Here, the performance gain from data fusion is mainly due to the selective use of information from the most appropriate view(s) and fusion provides a means to substantially reduce operator burden. The edge cases are defects that cannot be reliably detected in any one individual view without false alarms. Here certain fusion methods are shown to enable detection with reduced false alarms. In this context, fusion allows NDE capability to be extended with potential implications for the design and operation of engineering assets
Original languageEnglish
Article number20200086
Number of pages26
JournalProceedings of the Royal Society A: Mathematical and Physical Sciences
Volume476
Issue number2243
Early online date18 Nov 2020
DOIs
Publication statusPublished - Nov 2020

Structured keywords

  • Jean Golding

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