{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:07:38Z","timestamp":1768072058858,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T00:00:00Z","timestamp":1597622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004055","name":"King Fahd University of Petroleum and Minerals","doi-asserted-by":"publisher","award":["SR181028"],"award-info":[{"award-number":["SR181028"]}],"id":[{"id":"10.13039\/501100004055","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper presents a novel stator inter-turn fault diagnosis method for Line Start Permanent Magnet Synchronous Motors (LSPMSMs) using the frequency analysis of acoustic signals resulting from asymmetrical faults. In this method, acoustic data are experimentally collected from a 1 hp interior mount LSPMSM for different inter-turn fault cases and motor loading levels, while including the background noise. The signals are collected using a smartphone at a sampling rate of 48,000 samples per second. The signal for each case is analyzed using fast Fourier transform (FFT), which results in the decomposition of the signal into its frequency components. The results indicate that, for both no-load and full-load conditions, 39 components are observed to be affected by the faults, whereby their amplitudes increase with the fault severity. The 40-turns fault shows the highest difference in the component amplitudes compared with the healthy condition acoustic signal. Therefore, this diagnostic method is able to detect the stator inter-turn fault for interior mount LSPMSMs. Moreover, the method is simple and cheap since it uses a readily available sensor.<\/jats:p>","DOI":"10.3390\/sym12081370","type":"journal-article","created":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T21:58:53Z","timestamp":1597701533000},"page":"1370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Recognition of Stator Winding Inter-Turn Fault in Interior-Mount LSPMSM Using Acoustic Signals"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3565-398X","authenticated-orcid":false,"given":"Luqman S.","family":"Maraaba","sequence":"first","affiliation":[{"name":"Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7146-6698","authenticated-orcid":false,"given":"Ssennoga","family":"Twaha","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Faculty of Engineering, Kyambogo University, Kyambogo P.O. Box 01, Uganda"},{"name":"Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0982-2265","authenticated-orcid":false,"given":"Azhar","family":"Memon","sequence":"additional","affiliation":[{"name":"Center for Engineering Research, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}]},{"given":"Zakariya","family":"Al-Hamouz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Indiana Institute of Technology, Washington Blvd., Fort Wayne, IN 46803, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maraaba, L., Al-Hamouz, Z., and Abido, M. (2018). An efficient stator inter-turn fault diagnosis tool for induction motors. 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