Data-driven analysis of ultrasonic inspection data of pressure tubes

Panagiotis Zacharis, Graeme West, Gordon Dobie, Timothy Lardner, Anthony Gachagan

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
64 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)153-160
Number of pages8
JournalNuclear Technology
Volume202
Early online date1 Mar 2018
DOIs
Publication statusPublished - 30 Jun 2018

Keywords

  • CANDU
  • pressure tubes
  • ultrasonic inspection
  • machine learning
  • clustering

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