{"data":{"id":"10.21227/4jpc-qh81","type":"dois","attributes":{"doi":"10.21227/4jpc-qh81","prefix":"10.21227","suffix":"4jpc-qh81","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Haoxiang Xu","affiliation":[],"nameIdentifiers":[]},{"name":"Yu Fu","affiliation":[],"nameIdentifiers":[]},{"name":"Bin Luo","affiliation":[],"nameIdentifiers":[]},{"name":"Zicheng Liu","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"\"Three-phase PMSM with ITSC faults of stator winding Dataset\""}],"publisher":"IEEE DataPort","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\"Permanent magnet synchronous motors (PMSMs) are critical components for electromechanical energy conversion in renewable energy systems and electrified transportation. However, inter turn short-circuit (ITSC) faults in stator windings account for over 33% of motor failures, severely threatening system reliability and economic operation. High-performance artificial intelligence-based fault diagnosis methods rely on high-quality datasets that encompass diverse operating conditions and fault severity levels\\u2014a critical feature that is generally lacking in existing open-source data. To address this gap, this study presents and releases a novel dataset of inter-turn short-circuit faults in PMSMs. The dataset was collected from a custom-designed fault prototype, covering 12 torque-speed operating conditions, 9 levels of shorted turn percentages, and 3 short-circuit resistance values. It synchronously records three-phase voltage, three-phase current, and short-circuit current signals, thereby effectively simulating the gradual degradation process of stator winding insulation systems. Benchmark evaluations using a CNN-1D model systematically assess the performance of AI methods in terms of diagnostic accuracy and cross-operating condition transferability. The results demonstrate that this dataset effectively facilitates the training of AI models and yields reliable diagnostic outcomes, validating its value as a high-quality dataset. Furthermore, the dataset is promising for applications such as fault modeling validation, incipient fault diagnosis, transfer learning task design, and fault severity estimation, thereby filling a critical data gap in the power and energy domain.\"","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://ieee-dataport.org/documents/three-phase-pmsm-itsc-faults-stator-winding-dataset","contentUrl":null,"metadataVersion":0,"schemaVersion":null,"source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-02-28T04:28:02.000Z","registered":"2026-02-28T04:28:02.000Z","published":"2026","updated":"2026-02-28T04:28:02.000Z"},"relationships":{"client":{"data":{"id":"ieee.dataport","type":"clients"}},"provider":{"data":{"id":"ieee","type":"providers"}},"media":{"data":{"id":"10.21227/4jpc-qh81","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}