{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:10:38Z","timestamp":1771654238037,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T00:00:00Z","timestamp":1602806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KIST Europe","award":["11905"],"award-info":[{"award-number":["11905"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal false alarm rates, and the need for expert domain knowledge, which is costly. In this paper, we propose a novel data-driven health division method based on convolutional neural networks using a graphical representation of time series data, called a nested scatter plot. The proposed method trains the model with a small amount of labeled data and does not require a threshold value to predict the health state of rotary machines. Notwithstanding the lack of datasets that show the ground truth of health stages, our experiments with two open datasets of run-to-failure bearing demonstrated that our method is able to detect the early symptoms of bearing wear earlier and more efficiently than other threshold-based health indicator methods.<\/jats:p>","DOI":"10.3390\/s20205846","type":"journal-article","created":{"date-parts":[[2020,10,16]],"date-time":"2020-10-16T08:56:48Z","timestamp":1602838608000},"page":"5846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3723-1980","authenticated-orcid":false,"given":"Sungho","family":"Suh","sequence":"first","affiliation":[{"name":"Smart Convergence Group, Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH, 66123 Saarbruecken, Germany"},{"name":"Department of Computer Science, TU Kaiserslautern, 67663 Kaiserslautern, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3749-2999","authenticated-orcid":false,"given":"Joel","family":"Jang","sequence":"additional","affiliation":[{"name":"Smart Convergence Group, Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH, 66123 Saarbruecken, Germany"},{"name":"Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea"}]},{"given":"Seungjae","family":"Won","sequence":"additional","affiliation":[{"name":"Smart Convergence Group, Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH, 66123 Saarbruecken, Germany"},{"name":"Department of Software Convergence, Kyung Hee University, Yongin-si 17104, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6926-1386","authenticated-orcid":false,"given":"Mayank Shekhar","family":"Jha","sequence":"additional","affiliation":[{"name":"Centre de Recherche en Automatique de Nancy (CRAN), UMR 7039, CNRS, University of Lorraine, 54506 Vandoeuvre CEDEX, France"}]},{"given":"Yong Oh","family":"Lee","sequence":"additional","affiliation":[{"name":"Smart Convergence Group, Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH, 66123 Saarbruecken, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.ymssp.2017.05.024","article-title":"Dynamic modeling of gearbox faults: A review","volume":"98","author":"Liang","year":"2018","journal-title":"Mech. 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