Advanced defect detection algorithm using clustering in ultrasonic NDE
Gongzhang, Rui and Gachagan, Anthony (2016) Advanced defect detection algorithm using clustering in ultrasonic NDE. AIP Conference Proceedings, 1706. 180009. ISSN 1551-7616 (https://doi.org/10.1063/1.4940639)
Preview |
Text.
Filename: Gongzhang_Gachagan_AIP_2016_defect_detection_algorithm_using_clustering_in_ultrasonic_NDE.pdf
Final Published Version Download (512kB)| Preview |
Preview |
Text.
Filename: QNDE_2015_Advanced_Defect_Detection_Algorithm_Using_Clustering_in_Ultrasonic_NDE.pdf
Accepted Author Manuscript License: Unspecified Download (522kB)| Preview |
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.
ORCID iDs
Gongzhang, Rui ORCID: https://orcid.org/0000-0002-2487-0495 and Gachagan, Anthony ORCID: https://orcid.org/0000-0002-9728-4120;-
-
Item type: Article ID code: 57783 Dates: DateEvent10 February 2016Published10 September 2015AcceptedNotes: Paper originally presented at the 42nd Annual Review of Progress in Quantitative Nondestructive Evaluation, QNDE 2015, Incorporating the 6th European-American Workshop on Reliability of NDE; Hyatt Regency Minneapolis, Minneapolis; United States; 26 July 2015 through 31 July 2015. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 14 Sep 2016 08:30 Last modified: 11 Nov 2024 11:31 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/57783