Abstract
"Real-time sensor data for industrial equipment with maintenance history. Designed for developing predictive maintenance models, failure mode analysis, and maintenance planning systems."
Description
Dataset Overview
Comprehensive industrial maintenance dataset combining continuous sensor monitoring with historical maintenance records. Covers various equipment types in manufacturing environments with real-world failure modes and repair costs.
Equipment Coverage
- Motor-driven machinery (pumps, compressors, fans)
- Rotating equipment (bearings, gearboxes)
- Heat exchangers
- Hydraulic systems
- Electrical switchgear
Sensor Data
- Mechanical Sensors: Vibration (acceleration, velocity), temperature
- Electrical Sensors: Current draw (A), power consumption (kW), voltage (V)
- Operational Data: Run hours, speed (RPM), load percentage
- Maintenance Records: Work orders, repair costs, downtime hours
Data Characteristics
- Records: 10,000+ equipment observations
- Time Period: 2+ years operational history
- Failure Modes: 20+ documented failure types
- Label Balance: 70% normal, 30% failure-prone equipment
Research Applications
Predictive maintenance model development, remaining useful life estimation, failure classification, maintenance cost optimization, and condition-based maintenance planning.
Data Preview
| Equipment_ID | Timestamp | Vibration_mm_s | Temperature_C | Current_A | Run_Hours | Failure_Status |
|---|---|---|---|---|---|---|
| PUMP_001 | 2024-01-15 10:30 | 3.2 | 68.5 | 12.4 | 15840 | Normal |
| MOTOR_002 | 2024-01-15 10:30 | 8.9 | 92.3 | 18.7 | 24560 | Failure_Risk |
| PUMP_001 | 2024-01-15 11:00 | 3.5 | 69.1 | 12.6 | 15870 | Normal |
Showing first few rows for preview
Cite This Dataset
Kaggle (2026). Industrial Equipment Maintenance Data. [Dataset]. Kaggle. https://www.kaggle.com/datasets/mayurgadekar5555/industrial-equipment-maintenance-data
Source: Kaggle (2026)
Indexed by IoTDataset.com on Jan 13, 2026
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