Abstract
"Synthetic and real vibration signals from conveyor belt motors, simulating normal operation vs. bearing failure for IIoT maintenance."
Description
IIO Application
Monitoring the health of rotating machinery is the core of Industry 4.0. This dataset helps in detecting 'Early Bearing Failure' using acoustic and vibration sensors.
Metrics:
- Air Temp: Ambient temperature [K].
- Process Temp: Operating temperature [K].
- Rotational Speed: RPM.
- Torque: Applied force [Nm].
- Tool Wear: Accumulative wear time.
Data Preview
| Type | Air_Temp | Process_Temp | Rotational_Speed | Torque | Tool_Wear | Target |
|---|---|---|---|---|---|---|
| L | 298.1 | 308.6 | 1551 | 42.8 | 0 | 0 |
| L | 298.2 | 308.7 | 1408 | 46.3 | 3 | 0 |
| M | 298.1 | 308.5 | 1498 | 49.4 | 5 | 0 |
| H | 302.0 | 310.5 | 1350 | 65.2 | 120 | 1 |
Showing first few rows for preview
Cite This Dataset
External (2026). Industrial Conveyor Belt Motor Faults. [Dataset]. External. https://www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification
Source: External (2026)
Indexed by IoTDataset.com on Jan 10, 2026
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