| # 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. |
| |
