{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T21:39:52Z","timestamp":1763415592487,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Brandenburg Ministry of Science, Research and Cultural Affairs","award":["#22-F241-03-FhG\/007\/001"],"award-info":[{"award-number":["#22-F241-03-FhG\/007\/001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22155814","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T02:12:39Z","timestamp":1659665559000},"page":"5814","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Acoustic Resonance Testing of Small Data on Sintered Cogwheels"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2454-282X","authenticated-orcid":false,"given":"Yong Chul","family":"Ju","sequence":"first","affiliation":[{"name":"Cognitive Material Diagnostics Project Group of Fraunhofer Institute for Ceramic Technologies and Systems IKTS, 03046 Cottbus, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0670-4418","authenticated-orcid":false,"given":"Ivan","family":"Kraljevski","sequence":"additional","affiliation":[{"name":"Cognitive Material Diagnostics Project Group of Fraunhofer Institute for Ceramic Technologies and Systems IKTS, 03046 Cottbus, Germany"}]},{"given":"Heiko","family":"Neun\u00fcbel","sequence":"additional","affiliation":[{"name":"Condition Monitoring Hardware and Software Group of Fraunhofer Institute for Ceramic Technologies and Systems IKTS, 01109 Dresden, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3553-8412","authenticated-orcid":false,"given":"Constanze","family":"Tsch\u00f6pe","sequence":"additional","affiliation":[{"name":"Machine Learning and Data Analysis Group of Fraunhofer Institute for Ceramic Technologies and Systems IKTS, 01109 Dresden, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3895-7313","authenticated-orcid":false,"given":"Matthias","family":"Wolff","sequence":"additional","affiliation":[{"name":"Chair of Communications Engineering, Brandenburg University of Technology Cottbus-Senftenberg, 03046 Cottbus, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.3390\/s140101372","article-title":"Gearbox Tooth Cut Fault Diagnostics Using Acoustic Emission and Vibration Sensors\u2014A Comparative Study","volume":"14","author":"Qu","year":"2014","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2017.10.024","article-title":"Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine","volume":"140","author":"Haidong","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Lee, C., and You, W. 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