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--- |
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license: mit |
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tags: |
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- predictive-maintenance |
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- engine-failure-prediction |
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- adaboost |
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- classification |
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library_name: sklearn |
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--- |
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# Predictive Maintenance Model - Engine Failure Prediction |
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## Model Description |
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This model predicts engine failures for automotive predictive maintenance using sensor data. |
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**Model Type:** AdaBoost |
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**Task:** Binary Classification (Normal vs Faulty Engine) |
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**Framework:** scikit-learn / XGBoost |
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## Model Performance |
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### Test Set Metrics |
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- **Accuracy:** 0.6668 |
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- **Precision:** 0.6854 |
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- **Recall:** 0.8713 (Primary metric - minimizes false negatives) |
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- **F1-Score:** 0.7673 |
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- **ROC-AUC:** 0.6959 |
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## Model Details |
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### Hyperparameters |
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```python |
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{ |
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"learning_rate": 0.05, |
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"n_estimators": 100 |
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} |
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``` |
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### Training Information |
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- **Training Samples:** 15,628 |
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- **Test Samples:** 3,907 |
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- **Features:** 17 |
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- **Training Date:** 2026-01-25 11:47:47 |
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## Features |
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The model uses 17 features including: |
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- Engine RPM |
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- Lubricating oil pressure and temperature |
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- Fuel pressure |
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- Coolant pressure and temperature |
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- Engineered features (temperature-pressure ratios, differentials, etc.) |
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## Usage |
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```python |
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import joblib |
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from huggingface_hub import hf_hub_download |
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# Download model |
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model_path = hf_hub_download( |
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repo_id="SharleyK/predictive-maintenance-model", |
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filename="best_model.pkl" |
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) |
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# Load model |
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model = joblib.load(model_path) |
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# Download scaler |
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scaler_path = hf_hub_download( |
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repo_id="SharleyK/predictive-maintenance-model", |
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filename="scaler.pkl" |
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) |
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scaler = joblib.load(scaler_path) |
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# Make predictions |
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X_new_scaled = scaler.transform(X_new) |
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predictions = model.predict(X_new_scaled) |
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probabilities = model.predict_proba(X_new_scaled) |
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# Interpret results |
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# 0 = Normal/Healthy Engine |
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# 1 = Faulty/Requires Maintenance |
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``` |
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## Model Selection |
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This model was selected from 6 candidates: |
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- Decision Tree |
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- Bagging Classifier |
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- Random Forest |
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- AdaBoost |
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- Gradient Boosting |
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- XGBoost |
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Selection criteria: Highest test recall (to minimize false negatives - missed failures) |
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## Business Impact |
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- Reduces unplanned breakdowns by detecting failures early |
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- Minimizes emergency repair costs |
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- Optimizes maintenance scheduling |
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- Improves fleet availability and safety |
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## Limitations |
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- Requires all sensor inputs to be available |
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- Trained on specific engine types (automotive and small engines) |
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- Performance may degrade if sensor calibration changes |
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- Requires periodic retraining with new data |
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## Citation |
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``` |
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@model{predictive_maintenance_engine_model, |
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author = {SharleyK}, |
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title = {Predictive Maintenance Model - Engine Failure Prediction}, |
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year = {2026}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/SharleyK/predictive-maintenance-model} |
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} |
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``` |
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## License |
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MIT License |
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