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SharleyK/
predictive-maintenance-model

sklearn

predictive-maintenance

engine-failure-prediction

adaboost

classification

mit

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Predictive Maintenance Model - Engine Failure Prediction

Model Description

This model predicts engine failures for automotive predictive maintenance using sensor data.

Model Type: AdaBoost Task: Binary Classification (Normal vs Faulty Engine) Framework: scikit-learn / XGBoost

Model Performance

Test Set Metrics

Accuracy: 0.6668
Precision: 0.6854
Recall: 0.8713 (Primary metric - minimizes false negatives)
F1-Score: 0.7673
ROC-AUC: 0.6959

Model Details

Hyperparameters

python

{
  "learning_rate": 0.05,
  "n_estimators": 100
}

Training Information

Training Samples: 15,628
Test Samples: 3,907
Features: 17
Training Date: 2026-02-08 16:01:01

Features

The model uses 17 features including:

Engine RPM
Lubricating oil pressure and temperature
Fuel pressure
Coolant pressure and temperature
Engineered features (temperature-pressure ratios, differentials, etc.)

Usage

python

import joblib
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="SharleyK/predictive-maintenance-model",
    filename="best_model.pkl"
)

# Load model
model = joblib.load(model_path)

# Download scaler
scaler_path = hf_hub_download(
    repo_id="SharleyK/predictive-maintenance-model",
    filename="scaler.pkl"
)
scaler = joblib.load(scaler_path)

# Make predictions
X_new_scaled = scaler.transform(X_new)
predictions = model.predict(X_new_scaled)
probabilities = model.predict_proba(X_new_scaled)

# Interpret results
# 0 = Normal/Healthy Engine
# 1 = Faulty/Requires Maintenance

Model Selection

This model was selected from 6 candidates:

Decision Tree
Bagging Classifier
Random Forest
AdaBoost
Gradient Boosting
XGBoost

Selection criteria: Highest test recall (to minimize false negatives - missed failures)

Business Impact

Reduces unplanned breakdowns by detecting failures early
Minimizes emergency repair costs
Optimizes maintenance scheduling
Improves fleet availability and safety

Limitations

Requires all sensor inputs to be available
Trained on specific engine types (automotive and small engines)
Performance may degrade if sensor calibration changes
Requires periodic retraining with new data

Citation

@model{predictive_maintenance_engine_model,
  author = {SharleyK},
  title = {Predictive Maintenance Model - Engine Failure Prediction},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/SharleyK/predictive-maintenance-model}
}

License

MIT License