{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:06:21Z","timestamp":1782201981475,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.<\/jats:p>","DOI":"10.3390\/s23167023","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T12:45:53Z","timestamp":1691498753000},"page":"7023","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systems"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9316-6779","authenticated-orcid":false,"given":"Giovanni","family":"Donati","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1526-7715","authenticated-orcid":false,"given":"Michele","family":"Basso","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7421-5868","authenticated-orcid":false,"given":"Graziano A.","family":"Manduzio","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, 56122 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Mugnaini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0009-8154","authenticated-orcid":false,"given":"Tommaso","family":"Pecorella","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Camerota","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Florence, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.ymssp.2018.01.010","article-title":"Application of active magnetic bearings in flexible rotordynamic systems\u2014A state-of-the-art review","volume":"106","author":"Srinivas","year":"2018","journal-title":"Mech. 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