Predict early-stage failures in Electronic Control Units (ECUs) during the manufacturing process using Machine Learning!
✅ Reduce recalls & waste
✅ Increase production quality
✅ Improve real-time fault diagnostics
✨ Predict potential ECU failures before final testing
📊 Use advanced classification models (Random Forest, XGBoost)
🧠 Learn from sensor data, test results, and batch metadata
🛠 Easy to deploy & adapt to real-world ECU factories
📥 Data Collection → 🧹 Data Cleaning → ⚙️ Feature Engineering → 🤖 Model Training → 📈 Evaluation → 🪛 Deployment
yaml Copy Edit
| Step | Description |
|---|---|
| 🧽 Preprocessing | Remove outliers, fill nulls, normalize signals |
| 🧬 Feature Engineering | Convert raw sensor logs into meaningful predictors |
| 🔍 Modeling | Random Forest, XGBoost, or Logistic Regression |
| 📉 Evaluation | Accuracy, Precision, Recall, F1, ROC-AUC |
📦 ecu-failure-prediction/
├── 📂 data/ ← Raw and processed data
├── 📂 models/ ← Trained ML models (.pkl files)
├── 📂 notebooks/ ← Jupyter notebooks for EDA & training
├── 📂 src/ ← Python scripts (preprocess, train, evaluate)
├── 📄 requirements.txt ← Python dependencies
└── 📄 README.md ← You are here!
Note: The real dataset is not public. Use a simulated or anonymized dataset for testing.
| ECU_ID | Temperature | Voltage | Pressure | Test_Result | Failure |
|---|---|---|---|---|---|
| 1024 | 78.2°C | 12.3V | 1.05 bar | Pass | 0 |
| 1025 | 91.0°C | 11.7V | 0.98 bar | Fail | 1 |
git clone https://github.com/yourusername/ecu-failure-prediction.git
cd ecu-failure-prediction
pip install -r requirements.txt
python src/train.py --data data/ecu_data.csv --model models/rf_model.pkl
python src/evaluate.py --model models/rf_model.pkl
✅ Accuracy 92.5%
🎯 Precision 90.1%
🔁 Recall 89.7%
🧪 ROC-AUC 0.94
🛠️ Built With 🐍 Python 3.9
🧮 NumPy & Pandas
📊 Matplotlib & Seaborn
🤖 scikit-learn & XGBoost
📌Random Forest & Bagging Classifier
📓 Jupyter Notebook
1.📡 IoT integration for live data capture
2.🧠 Deep Learning (LSTM/Transformer-based models)
3.🌐 API endpoint (FastAPI/Flask) for real-time predictions
4.📊 Live dashboard (Streamlit or Power BI)
All ideas, issues, and pull requests are welcome! Please follow the structure and write clean code ✨
git checkout -b feature/YourFeature git commit -m "Add YourFeature" git push origin feature/YourFeature
This project is licensed under the MIT License. Feel free to fork, modify, and contribute!
Made with ❤️ by Shiva
📧 Email: shivauddav187@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/goshikauddav/
📁 Portfolio:https://uddavgoshika.github.io/Portfolio-Uddav/
⭐ Star this repo if you like it!
📢 Share it with others who care about smart manufacturing!