Skip to main content
Data in Brief / Mendeley Data

Triaxial Bearing Vibration Dataset of Induction Motor under Varying Load Conditions

Industrial IoT & Predictive Maintenance Industrial IoT / Predictive Maintenance
407 views
2 min read
License
Catalog metadata: This page is a discovery record, not publisher documentation. Verify the description, schema, provenance, version, licence, and citation at the linked source before use.

Catalog Summary

"Triaxial vibration time-series from an induction motor bearing under healthy and multiple fault severities (inner/outer race) at different mechanical loads, sampled at 10 kHz for condition monitoring research.[page:2][web:165]"

Catalog Notes

Overview

The Triaxial Bearing Vibration Dataset of Induction Motor under Varying Load Conditions provides high‑resolution vibration data from a three‑phase induction motor used to study bearing fault diagnosis in industrial IoT settings.[page:2][web:165]

Data Collection

  • Vibration signals were recorded using a MEMS‑based triaxial accelerometer attached to the motor housing near the drive‑end bearing, connected to an NI myRIO data acquisition system.[page:2]
  • The motor was operated with a healthy bearing and with bearings having inner‑race and outer‑race faults of different severities (e.g., 0.7 mm, 0.9 mm, 1.1 mm, 1.3 mm, 1.5 mm, 1.7 mm).[page:2]
  • Each bearing condition was tested at three load levels (100 W, 200 W, 300 W), resulting in 38 CSV files covering healthy and faulty states under varying loads.[page:2][web:165]

Signals and Format

  • Data were sampled at 10 kHz with 1000 samples per channel acquisition blocks.[page:2]
  • Each CSV file contains four columns: Time Stamp (seconds), X‑axis, Y‑axis, and Z‑axis vibration in units of g (1 g = 9.80665 m/s²).[page:2]
  • File names encode the bearing condition and load (e.g., Healthy-with-pulley.csv, 0.7inner-100 watt.csv, 1.7outer-300 watt.csv), enabling straightforward filtering by fault and load.[page:2]

Use Cases

  • Developing and benchmarking fault diagnosis algorithms for rotating machinery using supervised or unsupervised learning.[page:2][web:165]
  • Comparing time‑domain, frequency‑domain, and time–frequency (e.g., wavelet) features for bearing condition monitoring.[page:2]
  • Testing deep learning and domain adaptation approaches for industrial IoT condition monitoring under varying loads and fault severities.[page:2][web:165]

License

The article states that the datasets are stored on the Mendeley Data platform under DOI 10.17632/fm6xzxnf36.2; users should follow the reuse conditions and any license information given on the Mendeley dataset page and cite both the Data in Brief article and dataset DOI.[page:2][web:165]

View Data Structure

To explore column names, data types, and sample rows, visit the official dataset page on Data in Brief / Mendeley Data.

Preview on Data in Brief / Mendeley Data

Cite This Dataset

The dataset creators ask users of this dataset to cite the accompanying paper. Use one of the verified formats below.

Kumar, D. E. A. (2022). Triaxial bearing vibration dataset of induction motor under varying load conditions. Data in Brief, 42, 108315. http://dx.doi.org/10.17632/fm6xzxnf36.2

Source metadata: Data in Brief / Mendeley Data (2022)

Indexed by IoTDataset.com on Jan 28, 2026

Review the Source Record

Confirm the licence, version, access conditions, file format, and provenance at the source before use.

Open Source Page

Related Topics & Keywords

Browse all Industrial IoT & Predictive Maintenance datasets

Share This Research

More in Industrial IoT & Predictive Maintenance

View All
Industrial IoT Zenodo

MOIRA-UNIMORE Bearing Dataset — Independent Cart Systems [1.5 TB]

Large bearing condition-monitoring dataset for independent cart systems with vibration and system-variable signals. Zenodo archives total about 1.5 TB for fault diagnosis and PdM.

Jun 06, 2026
Industrial IoT Zenodo

Huawei Elevator Predictive Maintenance Dataset — IoT Door Sensors [453.9 kB]

Anonymized elevator-door IoT sensor time series from Huawei Munich Research Center. ZIP format, 453.9 kB, sampled at 4 Hz for predictive maintenance of elevator doors.

Jun 06, 2026
Industrial IoT Zenodo

Electric Motor Vibrations Dataset — Labeled Motor States [35.1 MB]

Electric-motor vibration sensor dataset from the CHIST-ERA SOON project. ZIP format, 35.1 MB, with labeled functioning states for ML predictive-maintenance experiments.

Jun 06, 2026
Industrial IoT Zenodo

VBL-VA001 — Lab-Scale Machine Vibration Fault Dataset [3.8 GB]

Lab-scale vibration dataset with 4,000 CSV files for normal, bearing fault, misalignment, and unbalance states. 3.8 GB ZIP for predictive-maintenance classification.

Jun 06, 2026
Industrial IoT Zenodo

I-BiDaaS CRF SCADA Dataset — Automotive Welding-Line Sensors [3.9 GB]

SCADA sensor dataset from automotive welding sub-assembly lines with 147 sensors. 3.9 GB 7z archive for anomaly thresholds and predictive maintenance analysis.

Jun 06, 2026
Industrial IoT Zenodo

Ball Bearing Vibration Metrics — Operational States Dataset [752.6 kB]

Labeled CSV dataset of ball-bearing vibration metrics captured every two minutes with an IFM VVB001 sensor. 752.6 kB for multi-class predictive-maintenance modeling.

Jun 06, 2026

Explore other topics

All topics →