Advanced defect detection algorithm using clustering in ultrasonic NDE

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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)

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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.

ORCID iDs

Gongzhang, Rui ORCID logoORCID: https://orcid.org/0000-0002-2487-0495 and Gachagan, Anthony ORCID logoORCID: https://orcid.org/0000-0002-9728-4120;
  • Item type:Article
    ID code:57783
    Dates:
    Date
    Event
    10 February 2016
    Published
    10 September 2015
    Accepted
    Notes: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:22 Dec 2024 01:18
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/57783
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