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 with 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 the result of 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.
Highlights
Multi-element ultrasonic arrays are widely used for the non-destructive evaluation (NDE) of safety-critical engineering components and structures across a range of industries [1]
Arrays were first used in NDE to replicate inspections that had previously been performed by scanning single-element, monolithic transducers
Sy et al [10] proposed the use of a specular echoes estimator (SEE) function, which provides an estimate of the amplitude of response that would be obtained at each point in each view if a planar specular reflector in a specified orientation existed at that point
Summary
Multi-element ultrasonic arrays are widely used for the non-destructive evaluation (NDE) of safety-critical engineering components and structures across a range of industries [1]. The overall ROC curves shown in figure 3a summarize the performance of each data fusion method when applied to a population of data that contains defect responses from equal numbers of each of the 49 defect types Note that this is an illustrative example based on the response of a defect at a single spatial location in the overall ROI; the actual ROC curve for a given defect in a real inspection would need to consider the possibility of that defect occurring at any spatial location in the ROI with the appropriate defect response for each location. The multiple best view and multiple modified matched filter techniques (black and red circles) generally perform worse than their single defect equivalents when applied to a population containing only one type of defect; this is because tests for defects not in the population tend to yield extra false positives
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