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--- |
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license: mit |
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language: |
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- en |
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library_name: scikit-learn |
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tags: |
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- predictive-maintenance |
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- random-forest |
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- binary-classification |
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- engine-maintenance |
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datasets: |
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- nasa-cmapss |
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metrics: |
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- accuracy |
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- f1 |
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- f2 |
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- roc-auc |
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--- |
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# Engine Predictive Maintenance Model |
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## Model Overview |
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This is a **Tuned Random Forest Classifier** trained for predictive engine maintenance with SMOTE oversampling to handle class imbalance and achieve high recall for failure detection. |
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## Model Details |
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- **Model Type**: Random Forest Classifier with SMOTE Pipeline |
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- **Framework**: scikit-learn, imbalanced-learn |
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- **Task**: Binary Classification (Engine Condition: Good/Failing) |
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- **Input Features**: 14 engineered sensor features (RPM, pressure, temperature, etc.) |
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- **Output**: Probability of engine failure (0-1) |
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## Model Performance |
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### Test Set Metrics |
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| Metric | Score | |
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|--------|-------| |
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| Accuracy | 0.6340 | |
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| Precision | 0.7456 | |
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| Recall | 0.6366 | |
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| F1 Score | 0.6868 | |
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| F2 Score | 0.6558 | |
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| ROC-AUC | 0.6893 | |
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| Brier Score | 0.2195 | |
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## Key Insights |
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- **High Recall (0.6366)**: Detects ~64% of actual failures |
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- **Competitive Precision (0.7456)**: ~75% of predictions are correct |
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- **Strong AUC (0.6893)**: Good discrimination between failure and non-failure cases |
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## Intended Use |
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This model is designed for: |
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- **Predictive Maintenance**: Identify engines at risk of failure before breakdown |
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- **Condition Monitoring**: Support data-driven maintenance decision-making |
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- **Fleet Management**: Optimize maintenance scheduling and resource allocation |
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- **Risk Assessment**: Provide failure probability scores for maintenance prioritization |
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## Limitations |
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- Trained on historical engine data with specific sensor configurations |
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- Performance may vary with new sensor types or operating conditions |
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- Model requires regular retraining with updated failure data |
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- Does not capture temporal degradation patterns (time-series) |
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- Assumes consistent sensor calibration and operating conditions |
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## Training Data |
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- **Dataset**: Engine Predictive Maintenance Dataset |
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- **Total Samples**: 19,581 engines |
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- **Training Samples**: 13,674 (70%) |
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- **Test Samples**: 3,907 (20%) |
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- **Features**: 14 engineered features (6 raw + 8 derived) |
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- **Class Distribution**: Imbalanced (Good: ~63%, Failure: ~37%) |
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## Training Procedure |
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1. Data preprocessing and feature engineering |
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2. Train-test split (70-20-10) |
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3. SMOTE oversampling on training data to handle class imbalance |
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4. Hyperparameter tuning via GridSearchCV with 5-fold cross-validation |
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5. Model evaluation on held-out test set |
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## Hyperparameters |
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- **n_estimators**: 400 |
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- **max_depth**: 12 |
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- **min_samples_leaf**: 4 |
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- **SMOTE k_neighbors**: 5 |
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- **Random state**: 42 |
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## Recommendations |
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1. **Threshold Tuning**: Adjust decision threshold based on cost of false positives vs. false negatives |
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2. **Continuous Monitoring**: Track model performance in production and retrain quarterly with new data |
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3. **Feature Importance**: Use SHAP or feature importance analysis to identify critical sensors |
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4. **Ensemble Approaches**: Consider combining with other models (XGBoost, LightGBM) for robust predictions |
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5. **Domain Expertise**: Combine predictions with expert knowledge for final maintenance decisions |
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## Citation |
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If you use this model, please cite: |
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``` |
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@misc{predictive-maintenance-model-2026, |
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title={Engine Predictive Maintenance Model}, |
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author={GreatLearning Capstone Team}, |
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year={2026}, |
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howpublished={Hugging Face Hub}, |
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url={https://huggingface.co/models/nilanjanadevc/engine-predictive-maintenance-model} |
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} |
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``` |
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## License |
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This model is released under the MIT License. See LICENSE file for details. |
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## Contact & Support |
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For questions or issues: |
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- GitHub: [Check repository](https://github.com/nilanjanadevc/predictive-engine-maintainence-mlops) |
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- Hugging Face: [@nilanjanadevc](https://huggingface.co/nilanjanadevc) |
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