Smart Manufacturing Maintenance Dataset
Catalog Summary
"Industrial equipment sensor data with real-time measurements for predictive maintenance and decision support. Combines temperature, vibration, pressure readings with maintenance costs and failure probability scores."
Catalog Notes
Dataset Purpose
Designed for developing predictive maintenance models for smart manufacturing systems. Integrates real-time sensor data with maintenance decision criteria to support proactive equipment servicing and failure prevention.
Key Features
- Real-Time Sensors: Temperature (°C), vibration (mm/s), pressure (PSI), acoustic emissions (dB)
- Maintenance Metrics: Inspection duration, technician availability, downtime cost
- Computed Features: Failure probability score (0-1 scale)
- Target Variable: Maintenance priority (High, Medium, Low)
Data Characteristics
- Records: 1,428 equipment observations
- Features: 10 columns
- File Size: 138.36 KB
- Format: CSV
- Preprocessing: Ready for immediate use
Applications
Equipment health diagnostics, preventive maintenance scheduling, failure risk assessment, multi-criteria decision making (MCDM) for maintenance planning, and intelligent factory management.
Data Preview
| Timestamp | Machine_ID | Temperature_C | Vibration_mm_s | Pressure_PSI | Failure_Probability | Maintenance_Priority |
|---|---|---|---|---|---|---|
| 2025-01-10 08:00 | M001 | 72.3 | 2.4 | 85.2 | 0.15 | Low |
| 2025-01-10 09:00 | M002 | 78.5 | 3.8 | 92.1 | 0.68 | High |
| 2025-01-10 10:00 | M003 | 65.1 | 1.9 | 79.5 | 0.32 | Medium |
Showing first few rows for preview
Provided Citation
Ziya07 (2025). Smart Manufacturing Maintenance Dataset. Kaggle. Retrieved from https://www.kaggle.com/datasets/ziya07/smart-manufacturing-maintenance-dataset
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Source metadata: External (2026)
Indexed by IoTDataset.com on Jan 13, 2026
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