Augmented Ultrasonic Data for Machine Learning

Research output: Contribution to journalArticleScientificpeer-review


Flaw detection in non-destructive testing, especially in complex signals like ultrasonic data, has thus far relied heavily on the expertise and judgement of trained human inspectors. While automated systems have been used for a long time, these have mostly been limited to using simple decision automation, such as signal amplitude threshold.

The recent advances in various machine learning algorithms have solved many similarly difficult classification problems, that have previously been considered intractable. For non-destructive testing, encouraging results have already been reported in the open literature, but the use of machine learning is still very limited in NDT applications in the field. Key issue hindering their use, is the limited availability of representative flawed data-sets to be used for training.

In the present paper, we develop modern, very deep convolutional network to detect flaws from phased-array ultrasonic data. We make extensive use of data augmentation to enhance the initially limited raw data and to aid learning. The data augmentation utilizes virtual flaws - a technique, that has successfully been used in training human inspectors and is soon to be used in nuclear inspection qualification. The results from the machine learning classifier are compared to human performance. We show, that using sophisticated data augmentation, modern deep learning networks can be trained to achieve superhuman performance by significant margin.
Original languageEnglish
Number of pages9
JournalNDT & E International
Publication statusSubmitted - 27 Mar 2019
MoE publication typeA1 Journal article-refereed

Fingerprint Dive into the research topics of 'Augmented Ultrasonic Data for Machine Learning'. Together they form a unique fingerprint.

  • Research Output

    Producing a POD curve with emulated signal response data

    Koskinen, T., Virkkunen, I., Papula, S., Sarikka, T. & Haapalainen, J., 2018, In : Insight: Non-Destructive Testing and Condition Monitoring. 60, 1, p. 42-48 7 p.

    Research output: Contribution to journalArticleScientificpeer-review

  • Cite this