| --- |
| license: mit |
| task_categories: |
| - tabular-classification |
| tags: |
| - predictive-maintenance |
| - iot |
| - sensors |
| - fleet-management |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Predictive Maintenance Engine Sensor Dataset |
|
|
| Engine sensor readings from commercial diesel vehicles for predictive maintenance classification. |
|
|
| ## Features |
|
|
| | Feature | Description | Unit | |
| |---------|-------------|------| |
| | Engine RPM | Engine revolutions per minute | RPM | |
| | Lub Oil Pressure | Lubrication oil pressure | bar | |
| | Fuel Pressure | Fuel delivery pressure | bar | |
| | Coolant Pressure | Cooling system pressure | bar | |
| | Lub Oil Temp | Lubrication oil temperature | °C | |
| | Coolant Temp | Engine coolant temperature | °C | |
| | Engine Condition | Target: 0=Normal, 1=Needs Maintenance | binary | |
|
|
| ## Dataset Splits |
|
|
| | Split | Samples | Purpose | |
| |-------|---------|---------| |
| | train | 75% | Model training | |
| | validation | 10% | Hyperparameter tuning | |
| | test | 15% | Final evaluation | |
|
|
| All splits are stratified by `Engine Condition` to maintain class balance. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| dataset = load_dataset("jskswamy/predictive-maintenance-data") |
| train_df = dataset["train"].to_pandas() |
| ``` |
|
|
| ## License |
|
|
| MIT License |
|
|
