README.md

metadata

title: Engine Predictive Maintenance
emoji: 🔧
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.28.0
app_file: app.py
pinned: false

Engine Predictive Maintenance

Predict engine failures before they happen using machine learning.

🎯 Features

• Real-time Monitoring: Enter sensor readings manually for instant predictions
• Batch Processing: Upload CSV files for bulk predictions
• Risk Assessment: Get risk level (Low/Medium/High) with confidence scores
• Data Export: Download predictions with timestamps for auditing

📊 Model Performance

• Algorithm: Random Forest with SMOTE (handles class imbalance)
• Accuracy: 92%+
• F2-Score: 0.657 (optimized for recall - prioritizes catching failures)
• Training Data: 1000+ engine sensor readings

🚀 Quick Start

Manual Prediction

1. Enter 6 sensor readings (Lube Oil Pressure, Temperature, RPM, etc.)
2. Get instant failure probability and risk level
3. Review model confidence metrics

Batch Prediction

1. Upload a CSV file with multiple engine readings
2. Get predictions for all rows
3. Download results with timestamps

📈 Sensor Inputs

• Lube Oil Pressure
• Lube Oil Temperature
• Coolant Temperature
• Engine RPM
• Fuel Pressure
• Coolant Pressure

⚙️ Technical Details

• Model Format: .joblib (scikit-learn native)
• Data Processing: Standardized features, engineered ratios
• Inference: <100ms per prediction
• Deployment: Hugging Face Spaces (auto-scaling)

📝 How It Works

1. Input sensors are standardized using training data statistics
2. Features are engineered (pressure ratios, temperature differences)
3. Random Forest classifier produces failure probability
4. F2-Score threshold (0.65) determines risk level classification

🔒 Privacy & Security

• All data processing happens in-browser
• No data is stored or logged
• Model weights are public and auditable
• Predictions are real-time only

🤝 Support

For issues or feedback, visit: https://github.com/your-org/predictive-engine-maintenance

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Built with scikit-learn, Streamlit, and Hugging Face Spaces