Domain adapted deep-learning for improved ultrasonic crack characterization using limited experimental data

Pyle, Richard J. and Bevan, Rhodri L. T. and Hughes, Robert R. and Ali, Amine Ait Si and Wilcox, Paul D. (2022) Domain adapted deep-learning for improved ultrasonic crack characterization using limited experimental data. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69 (4). 1485 - 1496. (https://doi.org/10.1109/TUFFC.2022.3151397)

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Abstract

Deep learning is an effective method for ultrasonic crack characterization due to its high level of automation and accuracy. Simulating the training set has been shown to be an effective method of circumventing the lack of experimental data common to nondestructive evaluation (NDE) applications. However, a simulation can neither be completely accurate nor capture all variability present in the real inspection. This means that the experimental and simulated data will be from different (but related) distributions, leading to inaccuracy when a deep learning algorithm trained on simulated data is applied to experimental measurements. This article aims to tackle this problem through the use of domain adaptation (DA). A convolutional neural network (CNN) is used to predict the depth of surface-breaking defects, with in-line pipe inspection as the targeted application. Three DA methods across varying sizes of experimental training data are compared to two non-DA methods as a baseline. The performance of the methods tested is evaluated by sizing 15 experimental notches of length (1–5 mm) and inclined at angles of up to 20° from the vertical. Experimental training sets are formed with between 1 and 15 notches. Of the DA methods investigated, an adversarial approach is found to be the most effective way to use the limited experimental training data. With this method, and only three notches, the resulting network gives a root-mean-square error (RMSE) in sizing of 0.5 ± 0.037 mm, whereas with only experimental data the RMSE is 1.5 ± 0.13 mm and with only simulated data it is 0.64 ± 0.044 mm.

  • Item type:Article
    ID code:84057
    Dates:
    Date
    Event
    30 April 2022
    Published
    14 February 2022
    Published Online
    10 February 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Mechanical engineering and machinery
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:07 Feb 2023 15:26
    Last modified:26 Apr 2024 06:34
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/84057
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