{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:46:17Z","timestamp":1775324777929,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T00:00:00Z","timestamp":1619481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT - Portuguese Foundation for Science and Technology","award":["PTDC\/EEI-EEE\/29494\/2017, UIDB\/04131\/2020, and UIDP\/04131\/2020."],"award-info":[{"award-number":["PTDC\/EEI-EEE\/29494\/2017, UIDB\/04131\/2020, and UIDP\/04131\/2020."]}]},{"name":"Operational Programme for Competitiveness and Internationalization (COMPETE 2020)","award":["POCI-01-0145-FEDER-029494"],"award-info":[{"award-number":["POCI-01-0145-FEDER-029494"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sources to perform supervised learning. An information fusion (IF) algorithm then creates a new feature set and performs the classification. Furthermore, this paper introduces a new method to achieve positive unlabeled learning. The flattened layer of several feature maps inputs the fuzzy c-means algorithm to perform a novelty detection instead of clusterization in a dynamic IF context. Experimental and on-site tests are reported with promising results.<\/jats:p>","DOI":"10.3390\/en14092509","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T21:18:20Z","timestamp":1619558300000},"page":"2509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8126-6067","authenticated-orcid":false,"given":"Andre S.","family":"Barcelos","sequence":"first","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, P-6201-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-6999","authenticated-orcid":false,"given":"Antonio J. Marques","family":"Cardoso","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, P-6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"ref_1","unstructured":"Cardoso, A.J.M. (2018). Diagnosis and Fault Tolerance of Electrical Machines, Power Electronics and Drives, IET."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Merizalde, Y., Hern\u00e1ndez-Callejo, L., and Duque-Perez, O. (2017). State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors. 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