Predictive Maintenance — Engine Failure Detection
Model Details
- Best Model : Random Forest
- Task : Binary Classification (0=Normal, 1=Faulty)
- Dataset : abhisekbasu/predictive-maintenance-engine
- Training Records: 15,628
- Test Records : 3,907
- Primary Metric : Recall (Faulty class)
Performance — Best Model (Random Forest)
| Metric | Score |
|---|---|
| Accuracy | 0.6473 |
| Precision | 0.6862 |
| Recall | 0.8116 |
| F1-Score | 0.7437 |
| ROC-AUC | 0.6750 |
All Models Comparison
| Model | Accuracy | Precision | Recall | F1 | ROC-AUC |
|---|---|---|---|---|---|
| Decision Tree | 0.5923 | 0.6789 | 0.6703 | 0.6746 | 0.5647 |
| Random Forest | 0.6473 | 0.6862 | 0.8116 | 0.7437 | 0.6750 |
| XGBoost | 0.6222 | 0.7052 | 0.6886 | 0.6968 | 0.6563 |
Features Used
- Original : Engine_RPM, Lub_Oil_Pressure, Fuel_Pressure, Coolant_Pressure, Lub_Oil_Temperature, Coolant_Temperature
- Engineered : Pressure_Index, Thermal_Index, Pressure_Temp_Ratio, RPM_Pressure_Ratio, Thermal_Deviation
Files
| File | Description |
|---|---|
| best_engine_model_v1.joblib | Trained Random Forest model |
| model_summary.json | Complete metrics and parameters |
| mlruns/mlruns.zip | All MLflow experiment logs |
Experiment Tracking
All hyperparameter combinations were tracked using MLflow. Complete experiment logs are available in mlruns/mlruns.zip.