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Engine Predictive Maintenance Model
Final Selected Model
GradientBoosting
Purpose
This model predicts engine condition using structured sensor readings.
Input Features
- Engine rpm
- Lub oil pressure
- Fuel pressure
- Coolant pressure
- lub oil temp
- Coolant temp
Output
- Engine Condition
- 0 = normal
- 1 = maintenance required
Final Selection Metrics
- Sensitivity: 0.869265
- Specificity: 0.315789
- F1 Score: 0.765737
- ROC-AUC: 0.700411
- Balanced Selection Score: 0.693305
Best Tuned Hyperparameters
{ "learning_rate": 0.05, "max_depth": 2, "n_estimators": 100 }
Notes
This model was selected using a risk-aware evaluation framework that considered:
- sensitivity
- specificity
- F1 score
- ROC-AUC
- generalization behavior
The model is intended as an early-warning decision-support tool for predictive maintenance.
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