{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:14:32Z","timestamp":1775913272153,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Basic Science Research Program through the National Research Foundation of Korea (NRF) by the Ministry of Science, Information and Communication Technology (ICT) and Future Planning in Korea","award":["2020R1A2B5B02001717"],"award-info":[{"award-number":["2020R1A2B5B02001717"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Any bearing faults are a leading cause of motor damage and bring economic losses. Fast and accurate identification of bearing faults is valuable for preventing damaging the whole equipment and continuously running industrial processes without interruption. Vibration signals from a running motor can be utilized to diagnose a bearing health condition. This study proposes a detection method for bearing faults based on two types of neural networks from motor vibration data. The proposed method uses an autoencoder neural network for constructing a new motor vibration feature and a feed-forward neural network for the final detection. The constructed signal feature enhances the prediction performance by focusing more on a fault type that is difficult to detect. We conducted experiments on the CWRU bearing datasets. The experimental study shows that the proposed method improves the performance of the feed-forward neural network and outperforms the other machine learning algorithms.<\/jats:p>","DOI":"10.3390\/s24102978","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T12:27:11Z","timestamp":1715171231000},"page":"2978","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Unsupervised Feature-Construction-Based Motor Fault Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"given":"Tsatsral","family":"Amarbayasgalan","sequence":"first","affiliation":[{"name":"Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0394-9054","authenticated-orcid":false,"given":"Keun Ho","family":"Ryu","sequence":"additional","affiliation":[{"name":"Data Science Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.ymssp.2016.02.007","article-title":"Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals","volume":"76","author":"Li","year":"2016","journal-title":"Mech. 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