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Acoustic Resonance Testing of Small Data on Sintered Cogwheels

  • Based on the fact that cogwheels are indispensable parts in manufacturing, we present the acoustic resonance testing (ART) of small data on sintered cogwheels for quality control in the context of non-destructive testing (NDT). Considering the lack of extensive studies on cogwheel data by means of ART in combination with machine learning (ML), we utilize time-frequency domain feature analysis and apply ML algorithms to the obtained feature sets in order to detect damaged samples in two ways: one-class and binary classification. In each case, despite small data, our approach delivers robust performance: All damaged test samples reflecting real-world scenarios are recognized in two one-class classifiers (also called detectors), and one intact test sample is misclassified in binary ones. This shows the usefulness of ML and time-frequency domain feature analysis in ART on a sintered cogwheel dataset.

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Metadaten
Author: Yong Chul Ju, Ivan Kraljevski, Heiko Neunübel, Constanze Tschöpe, Matthias WolffORCiD
DOI:https://doi.org/10.3390/s22155814
ISSN:1424-8220
Title of the source (English):Sensors
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2022
Tag:acoustic resonance testing (ART) non-destructive testing (NDT); deep learning; machine learning; non-destructive testing (NDT); small-data
Volume/Year:22
Issue number:15
Article number:5814
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Kommunikationstechnik
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