{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:55:06Z","timestamp":1778345706459,"version":"3.51.4"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":27,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["2021R1A4A2001824"],"award-info":[{"award-number":["2021R1A4A2001824"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["NRF-2021M2E6A1084687"],"award-info":[{"award-number":["NRF-2021M2E6A1084687"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Fault diagnosis of rolling element bearings (REBs), one type of essential mechanical element, has been actively researched; recent research has focused on the use of deep-learning-based approaches. However, conventional deep-learning-based fault-diagnosis approaches are vulnerable to various operating speeds, which greatly affect the vibration characteristics of the system studied. To solve this problem, previous deep-learning-based studies have usually been carried out by increasing the complexity of the model or diversifying the task of the model. Still, limitations remain because the reason of increasing complexity is unclear and the roles of multiple tasks are not well-defined. Therefore, this study proposes a multi-head de-noising autoencoder-based multi-task model for robust diagnosis of REBs under various speed conditions. The proposed model employs a multi-head de-noising autoencoder and multi-task learning strategy to robustly extract features under various speed conditions, while effectively disentangling the speed- and fault-related information. In this research, we evaluate the proposed method using the signals measured from bearing experiments under various speed conditions. The results of the evaluation study show that the proposed method outperformed conventional methods, especially when the training and test datasets have large discrepancies in their operating conditions.<\/jats:p>","DOI":"10.1093\/jcde\/qwad076","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:26:37Z","timestamp":1690849597000},"page":"1804-1820","source":"Crossref","is-referenced-by-count":13,"title":["Multi-head de-noising autoencoder-based multi-task model for fault diagnosis of rolling element bearings under various speed conditions"],"prefix":"10.1093","volume":"10","author":[{"given":"Jongmin","family":"Park","sequence":"first","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Seoul National University , Seoul 08826 , Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinoh","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Seoul National University , Seoul 08826 , Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taehyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Seoul National University , Seoul 08826 , Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2796-055X","authenticated-orcid":false,"given":"Jong Moon","family":"Ha","sequence":"additional","affiliation":[{"name":"Intelligent Wave Engineering Team, Korea Research Institute of Standards and Science (KRISS) , Daejeon 34113 , Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0135-3660","authenticated-orcid":false,"given":"Byeng D","family":"Youn","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Seoul National University , Seoul 08826 , Republic of Korea"},{"name":"Institute of Advanced Machines and Design, Seoul National University , Seoul 08826 , Republic of Korea"},{"name":"OnePredict Inc. , Seoul 06160 , Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"2023081216231470700_bib1","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1177\/1077546320929141","article-title":"Bearing fault diagnosis using deep learning techniques coupled with handcrafted feature extraction: A comparative study","volume":"27","author":"Alabsi","year":"2021","journal-title":"Journal of Vibration and Control"},{"key":"2023081216231470700_bib2","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1115\/1.1569940","article-title":"A stochastic model for simulation and diagnostics of rolling element bearings with localized faults","volume":"125","author":"Antoni","year":"2003","journal-title":"Journal of Vibration and Acoustics"},{"key":"2023081216231470700_bib3","doi-asserted-by":"crossref","first-page":"408","DOI":"10.7551\/mitpress\/4071.003.0015","article-title":"Learning functional relations based on experience with input-output pairs by humans and artificial neural networks","volume-title":"Studies in cognition. 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