{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T14:46:46Z","timestamp":1785336406041,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Researchers","award":["PNURSP2022R178"],"award-info":[{"award-number":["PNURSP2022R178"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.<\/jats:p>","DOI":"10.3390\/s22218210","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T09:59:37Z","timestamp":1666778377000},"page":"8210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data"],"prefix":"10.3390","volume":"22","author":[{"given":"Sajal","family":"Misra","sequence":"first","affiliation":[{"name":"Mechanical Engineering, Galgotias College of Engineering and Technology, Dr. A.P.J. Abdul Kalam Technical University, Greater Noida 201306, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-0952","authenticated-orcid":false,"given":"Satish","family":"Kumar","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India"},{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-5350","authenticated-orcid":false,"given":"Sameer","family":"Sayyad","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1942-9179","authenticated-orcid":false,"given":"Arunkumar","family":"Bongale","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5439-5437","authenticated-orcid":false,"given":"Priya","family":"Jadhav","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India"},{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0169-6738","authenticated-orcid":false,"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[{"name":"Machine Intelligence Research Laboratories, Auburn, WA 98071, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6910-6861","authenticated-orcid":false,"given":"Lubna Abdelkareim","family":"Gabralla","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.08.036","article-title":"Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals","volume":"113","author":"Glowacz","year":"2018","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.eswa.2018.04.025","article-title":"Bearing faults diagnosis using fuzzy expert system relying on an improved range overlaps and similarity method","volume":"108","author":"Berredjem","year":"2018","journal-title":"Expert Syst. 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