{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:28:21Z","timestamp":1771025301844,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lower Austria Science Call of GFF","award":["SC18-023"],"award-info":[{"award-number":["SC18-023"]}]},{"name":"Lower Austria","award":["WST3-F-5030642\/006-2018"],"award-info":[{"award-number":["WST3-F-5030642\/006-2018"]}]},{"DOI":"10.13039\/501100004955","name":"Austrian Research Promotion Agency","doi-asserted-by":"publisher","award":["project 883301"],"award-info":[{"award-number":["project 883301"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rolling element bearing faults significantly contribute to overall machine failures, which demand different strategies for condition monitoring and failure detection. Recent advancements in machine learning even further expedite the quest to improve accuracy in fault detection for economic purposes by minimizing scheduled maintenance. Challenging tasks, such as the gathering of high quality data to explicitly train an algorithm, still persist and are limited in terms of the availability of historical data. In addition, failure data from measurements are typically valid only for the particular machinery components and their settings. In this study, 3D multi-body simulations of a roller bearing with different faults have been conducted to create a variety of synthetic training data for a deep learning convolutional neural network (CNN) and, hence, to address these challenges. The vibration data from the simulation are superimposed with noise collected from the measurement of a healthy bearing and are subsequently converted into a 2D image via wavelet transformation before being fed into the CNN for training. Measurements of damaged bearings are used to validate the algorithm\u2019s performance.<\/jats:p>","DOI":"10.3390\/s22072490","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T23:31:43Z","timestamp":1648164703000},"page":"2490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6905-2843","authenticated-orcid":false,"given":"Matthias","family":"Kahr","sequence":"first","affiliation":[{"name":"Department for Integrated Sensor Systems, University for Continuing Education Krems, 2700 Wiener Neustadt, Austria"}]},{"given":"Gabor","family":"Kov\u00e1cs","sequence":"additional","affiliation":[{"name":"Department for Integrated Sensor Systems, University for Continuing Education Krems, 2700 Wiener Neustadt, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0868-6954","authenticated-orcid":false,"given":"Markus","family":"Loinig","sequence":"additional","affiliation":[{"name":"Senzoro GmbH, 1030 Wien, Austria"}]},{"given":"Hubert","family":"Br\u00fcckl","sequence":"additional","affiliation":[{"name":"Department for Integrated Sensor Systems, University for Continuing Education Krems, 2700 Wiener Neustadt, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0378-7796(02)00172-4","article-title":"Induction machine drive condition monitoring and diagnostic research\u2014A survey","volume":"64","author":"Singh","year":"2003","journal-title":"Electr. 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