Data-driven analysis of ultrasonic inspection data of pressure tubes

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Zacharis, Panagiotis and West, Graeme and Dobie, Gordon and Lardner, Timothy and Gachagan, Anthony (2018) Data-driven analysis of ultrasonic inspection data of pressure tubes. Nuclear Technology, 202. pp. 153-160. ISSN 1943-7471 (https://doi.org/10.1080/00295450.2017.1421803)

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Abstract

Pressure tubes are critical components of CANDU reactors and other pressurized heavy water–type reactors because they contain the nuclear fuel and the coolant. Manufacturing flaws as well as defects developed during in-service operation can lead to coolant leakage and can potentially damage the reactor. The current inspection process of these flaws is based on manually analyzing ultrasonic data received from multiple probes during planned, statutory outages. Recent advances in ultrasonic inspection tools enable the provision of high-resolution data of significantly large volumes. This highlights the need for an efficient autonomous signal analysis process. Typically, automation of ultrasonic inspection data analysis is approached by knowledge-based or supervised data-driven methods. This work proposes an unsupervised data-driven framework that requires no explicit rules or individually labeled signals. The framework follows a two-stage clustering procedure that utilizes the Density-Based Spatial Clustering of Applications with Noise density-based clustering algorithm and aims to provide decision support for the assessment of potential defects in a robust and consistent way. Nevertheless, verified defect dimensions are essential in order to assess the results and train the framework for unseen defects. Initial results of the implementation are presented and discussed, with the method showing promise as a means of assessing ultrasonic inspection data.

ORCID iDs

Zacharis, Panagiotis ORCID logoORCID: https://orcid.org/0000-0003-1682-3362, West, Graeme ORCID logoORCID: https://orcid.org/0000-0003-0884-6070, Dobie, Gordon ORCID logoORCID: https://orcid.org/0000-0003-3972-5917, Lardner, Timothy and Gachagan, Anthony ORCID logoORCID: https://orcid.org/0000-0002-9728-4120;
  • Item type:Article
    ID code:63435
    Dates:
    Date
    Event
    30 June 2018
    Published
    1 March 2018
    Published Online
    4 December 2017
    Accepted
    28 September 2017
    Submitted
    Notes:This is an Accepted Manuscript of an article published by Taylor & Francis in Nuclear Technology on 01 March 2018, available online: http://www.tandfonline.com/10.1080/00295450.2017.1421803.
    Subjects:Science > Physics
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Strategic Research Themes > Energy
    Depositing user: Pure Administrator
    Date deposited:07 Mar 2018 12:38
    Last modified:27 Nov 2024 01:13
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/63435
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