{"data":{"id":"10.17632/42v3s74gf9.1","type":"dois","attributes":{"doi":"10.17632/42v3s74gf9.1","prefix":"10.17632","suffix":"42v3s74gf9.1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Bourdalos, Dimitrios","nameType":"Personal","givenName":"Dimitrios","familyName":"Bourdalos","affiliation":["University of Patras"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3031-1128","nameIdentifierScheme":"ORCID"}]},{"name":"Sakellariou, John","nameType":"Personal","givenName":"John","familyName":"Sakellariou","affiliation":["University of Patras"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3027-8284","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"UPATRAS Rotating Machinery Vibration Dataset for Incipient Fault Diagnosis under Varying Rotating Speed"}],"publisher":"Mendeley Data","container":{},"publicationYear":2026,"subjects":[{"subject":"Machine Learning"},{"subject":"System Fault Diagnosis"},{"subject":"Vibration Analysis"},{"subject":"Vibration Condition Monitoring"},{"subject":"System Fault Detection"},{"subject":"Deep Learning"},{"subject":"Condition-Based Maintenance"},{"subject":"Fault Diagnosis"},{"subject":"Intelligent Fault Diagnosis"},{"subject":"Machinery Fault Diagnosis"}],"contributors":[{"name":"University of Patras","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[{"schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/017wvtq80","nameIdentifierScheme":"ROR"}]}],"dates":[{"date":"2026-04-17T14:54:15Z","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.17632/42v3s74gf9","resourceTypeGeneral":"Dataset","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsUri":"info:eu-repo/semantics/openAccess"},{"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 vibration measurements acquired from a rotating machinery test rig developed at the University of Patras, Greece, at the Stochastic Mechanical Systems and Automation (SMSA) Laboratory. The test rig consists of two foot-mounted electric motors coupled via a claw clutch and instrumented with a single uniaxial accelerometer mounted on the drive motor. The dataset has been designed for research on vibration-based condition monitoring, fault detection, fault diagnosis, signal processing, feature extraction, machine learning, deep learning, and related data-driven methodologies for rotating machinery operating under varying speed conditions. In contrast to many rotating machinery datasets that focus on a limited number of operating conditions, the present dataset provides dense coverage of a wide rotating-speed range.\n\nThe dataset comprises eight machinery states, namely one healthy state and seven incipient fault scenarios associated with three fault families: limited unbalance, mechanical looseness, and coupler wear. The limited unbalance scenarios are implemented by replacing the main coupler mounting bolt with a heavier bolt, leading to two fault levels, denoted as Unbalance 3g and Unbalance 5g. The mechanical looseness scenarios are implemented through torque reduction of the drive-motor mounting bolts A and B, leading to four fault cases: Bolt A 50%, Bolt A 100%, Bolt B 50%, and Bolt B 100%. The coupler wear scenario corresponds to incipient wear at the base of a single spider tooth of the claw clutch. The healthy state is characterized by mounting torques [A,B] = [5,5] N m, while the looseness scenarios are defined through the corresponding reduced torque values.\n\nThe measurements are acquired under 75 different rotating speeds ranging from 35.0 Hz to 49.8 Hz with a step of 0.2 Hz. Four measurement sequences are provided for the healthy state and five for each faulty state, leading to a total of 2925 individual vibration signals. Each signal contains 3500 samples, corresponding to 3.42 s with sampling frequency 1024 Hz and frequency bandwidth [0 - 512] Hz. The dataset is provided entirely in CSV format. \n\nIf you use this dataset in your work, please cite the following publication: https://doi.org/10.1016/j.ymssp.2025.113204\n\nFurther details on the data are available in the README.pdf file included in this dataset.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Hellenic Foundation for Research and Innovation","funderIdentifier":"https://ror.org/05v75r592","funderIdentifierType":"ROR"}],"xml":"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