Engine Predictive Maintenance Model

Model Overview

Model Details

• Model Type: Random Forest Classifier with SMOTE Pipeline
• Framework: scikit-learn, imbalanced-learn
• Task: Binary Classification (Engine Condition: Good/Failing)
• Input Features: 14 engineered sensor features (RPM, pressure, temperature, etc.)
• Output: Probability of engine failure (0-1)

Model Performance

Test Set Metrics

Key Insights

• High Recall (0.6366): Detects ~64% of actual failures
• Competitive Precision (0.7456): ~75% of predictions are correct
• Strong AUC (0.6893): Good discrimination between failure and non-failure cases

Intended Use

• Predictive Maintenance: Identify engines at risk of failure before breakdown
• Condition Monitoring: Support data-driven maintenance decision-making
• Fleet Management: Optimize maintenance scheduling and resource allocation
• Risk Assessment: Provide failure probability scores for maintenance prioritization

Limitations

• Trained on historical engine data with specific sensor configurations
• Performance may vary with new sensor types or operating conditions
• Model requires regular retraining with updated failure data
• Does not capture temporal degradation patterns (time-series)
• Assumes consistent sensor calibration and operating conditions

Training Data

• Dataset: Engine Predictive Maintenance Dataset
• Total Samples: 19,581 engines
• Training Samples: 13,674 (70%)
• Test Samples: 3,907 (20%)
• Features: 14 engineered features (6 raw + 8 derived)
• Class Distribution: Imbalanced (Good: ~63%, Failure: ~37%)

Training Procedure

1. Data preprocessing and feature engineering
2. Train-test split (70-20-10)
3. SMOTE oversampling on training data to handle class imbalance
4. Hyperparameter tuning via GridSearchCV with 5-fold cross-validation
5. Model evaluation on held-out test set

Hyperparameters

• n_estimators: 400
• max_depth: 12
• min_samples_leaf: 4
• SMOTE k_neighbors: 5
• Random state: 42

Recommendations

1. Threshold Tuning: Adjust decision threshold based on cost of false positives vs. false negatives
2. Continuous Monitoring: Track model performance in production and retrain quarterly with new data
3. Feature Importance: Use SHAP or feature importance analysis to identify critical sensors
4. Ensemble Approaches: Consider combining with other models (XGBoost, LightGBM) for robust predictions
5. Domain Expertise: Combine predictions with expert knowledge for final maintenance decisions

Citation

@misc{predictive-maintenance-model-2026,
  title={Engine Predictive Maintenance Model},
  author={GreatLearning Capstone Team},
  year={2026},
  howpublished={Hugging Face Hub},
  url={https://huggingface.co/models/nilanjanadevc/engine-predictive-maintenance-model}
}

License

Contact & Support

• GitHub: Check repository
• Hugging Face: @nilanjanadevc