Augmented Ultrasonic Data for Machine Learning

Iikka Virkkunen*, Tuomas Koskinen, Oskari Jessen-Juhler, Jari Rinta-aho

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

2 Sitaatiot (Scopus)
13 Lataukset (Pure)

Abstrakti

Flaw detection in non-destructive testing, especially for 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 con- sidered intractable. For non-destructive testing, encouraging results have al- ready 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, 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 success- fully 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 aug- mentation, modern deep learning networks can be trained to achieve human- level performance.
AlkuperäiskieliEnglanti
Artikkeli4
Sivumäärä11
JulkaisuJournal of Nondestructive Evaluation
Vuosikerta40
Numero1
Varhainen verkossa julkaisun päivämäärä2 tammikuuta 2021
DOI - pysyväislinkit
TilaJulkaistu - maaliskuuta 2021
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

Sormenjälki Sukella tutkimusaiheisiin 'Augmented Ultrasonic Data for Machine Learning'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä