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Abstract
A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed Ascans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as 'legitimate reflector' or 'artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone.
Original language | English |
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Article number | 180009 |
Number of pages | 9 |
Journal | AIP Conference Proceedings |
Volume | 1706 |
DOIs | |
Publication status | Published - 10 Feb 2016 |
Event | 42nd Annual Review of Progress in Quantitative Nondestructive Evaluation - Minneapolis, Minnesota, United States Duration: 26 Jul 2015 → 31 Jul 2015 |
Keywords
- defect detection
- ultrasonic NDE
- classification algorithm
- probability of defection
- probability of false alarm
- scattering noise
- ultrasonic non-destructive evaluation
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Dive into the research topics of 'Advanced defect detection algorithm using clustering in ultrasonic NDE'. Together they form a unique fingerprint.Projects
- 1 Finished
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New Methods For Ultrasonic NDE Of Difficult Materials
Gachagan, A. (Principal Investigator), Li, M. (Co-investigator), Mulholland, A. (Co-investigator) & O'Leary, R. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/08/11 → 31/07/14
Project: Research