{"data":{"id":"10.21227/3n2f-8z47","type":"dois","attributes":{"doi":"10.21227/3n2f-8z47","prefix":"10.21227","suffix":"3n2f-8z47","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Yunus Emre ACAR","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"\"RADAR I\\/Q DATA FOR MACHINERY IMBALANCE DETECTION\""}],"publisher":"IEEE DataPort","container":{},"publicationYear":2025,"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":"\"This dataset contains continuous-wave (CW) radar recordings of motor vibration collected to support the detection and characterization of rotational imbalance. A total of 1,802 independent experiments were performed at a sensor-to-target distance of 30 cm; each experiment comprises 30 seconds of baseband in-phase (I) and quadrature (Q) samples recorded at 10 kHz (300,000 samples per channel). During each recording, motor speed and load were held constant; experimental conditions spanned speeds from 500 to 1500 rpm and loads from 0 to 3 Nm. Controlled rotational imbalance was introduced by coupling a disk to the motor and attaching discrete masses, producing four labeled classes: baseline (0 g), light imbalance (10 g), medium imbalance (20 g), and severe imbalance (30 g). Data are organized in a ZIP archive containing four class-specific folders. The dataset is intended for supervised classification, regression of imbalance severity, spectral\\/time\\u2011frequency analysis, demodulation, and feature\\u2011extraction method development, and evaluation of noncontact sensing approaches for rotor health monitoring.Radar frequency: 24 GHz (CW mode)Sampling frequency: 10 kHzMeasurement duration: 30 secondsSignal length: 300,000 samples (I and Q channels)Motor speed: 500-1500 rpm (11 equally spaced levels)Load torque: 0-3 Nm (6 equally spaced levels)Dataset Structure:Total number of experiments: 1802 (# of experiments per classes \u0026nbsp;{462, 464, 413, 463})Data formats: CSVRaw I\\/Q signalsThis dataset can be utilized for mass imbalance fault diagnosis, machine learning algorithm development, and signal processing method validation in rotating machinery condition monitoring applications.\"","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://ieee-dataport.org/documents/radar-iq-data-machinery-imbalance-detection","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":"2025-08-16T14:18:36.000Z","registered":"2025-08-16T14:18:36.000Z","published":"2025","updated":"2025-08-16T14:18:36.000Z"},"relationships":{"client":{"data":{"id":"ieee.dataport","type":"clients"}},"provider":{"data":{"id":"ieee","type":"providers"}},"media":{"data":{"id":"10.21227/3n2f-8z47","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}