2026-10-15T22:23:57Z https://data.mendeley.com/oai

oai:data.mendeley.com/42v3s74gf9.1 2026-04-17T14:54:15Z

Bourdalos, Dimitrios UPATRAS Rotating Machinery Vibration Dataset for Incipient Fault Diagnosis under Varying Rotating Speed Mendeley Data 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. The 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. The 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. If you use this dataset in your work, please cite the following publication: https://doi.org/10.1016/j.ymssp.2025.113204 Further details on the data are available in the README.pdf file included in this dataset. Machine Learning System Fault Diagnosis Vibration Analysis Vibration Condition Monitoring System Fault Detection Deep Learning Condition-Based Maintenance Fault Diagnosis Intelligent Fault Diagnosis Machinery Fault Diagnosis Sakellariou, John Dataset doi:10.17632/42v3s74gf9.1 oai:data.mendeley.com/42v3s74gf9.1 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/42v3s74gf9 2026-04-17T14:54:15Z