This repository contains comprehensive data analytics implementations for mechanical engineering domains, incorporating Predictive, Prescriptive, Descriptive, and Diagnostic Analytics approaches using Python, Machine Learning, and Deep Learning techniques.
- Implement advanced analytics techniques across multiple mechanical engineering domains
- Leverage large-scale datasets for extracting meaningful insights
- Develop robust ML/DL models for real-world engineering problems
- Provide end-to-end implementations from data preprocessing to deployment
- Domain: Thermal Systems & HVAC
- Analytics Type: Predictive + Prescriptive Analytics
- Use Case: Energy consumption forecasting and HVAC optimization
- Key Techniques:
- Time-series forecasting (LSTM, Prophet, ARIMA)
- Energy consumption prediction
- Prescriptive recommendations for energy optimization
- Anomaly detection in thermal systems
- Domain: Manufacturing & Quality Control
- Analytics Type: Prescriptive + Descriptive Analytics
- Use Case: CNC machining optimization and predictive quality control
- Key Techniques:
- Tool wear prediction
- Parameter optimization using Reinforcement Learning
- Statistical process control
- Quality defect classification using Deep Learning
- Domain: HVAC (Heating, Ventilation, Air Conditioning)
- Analytics Type: Descriptive + Diagnostic + Predictive Analytics
- Use Case: Analyzing parameters affecting heating and coolng loads
- Key Techniques:
- Analyze HVAC performance
- Identify inefficiencies
- Diagnostic pattern recognition
- Energy consumption prediction
- Programming Language: Python 3.8+
- ML/DL Frameworks: TensorFlow, PyTorch, Scikit-learn
- Data Processing: Pandas, NumPy, SciPy
- Visualization: Matplotlib, Seaborn, Plotly
- Signal Processing: PyWavelets, librosa
- Optimization: CVXPY, SciPy Optimize
# Clone the repository
git clone https://github.com/kartik-commits/mechanical-engineering-analytics.git
cd mechanical-engineering-analytics
# Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
## 📊 Dataset Guidelines
All datasets follow these principles:
1. **Large-scale data** suitable for ML/DL applications
2. **Real-world or benchmark datasets** from recognized organizations
3. **Domain-specific** from mechanical engineering fields
4. **Appropriate sampling rates** (e.g., 5-20 kHz for vibration data)
5. **Sufficient resolution** for robust model development
## 🎓 Analytics Approaches
### Predictive Analytics
- Forecasting future outcomes using historical data
- Time-series prediction models
- Failure prediction and RUL estimation
### Prescriptive Analytics
- Recommending optimal actions
- Parameter optimization
- Decision support systems
### Descriptive Analytics
- Understanding historical patterns
- Statistical analysis and visualization
- Performance benchmarking
### Diagnostic Analytics
- Root cause analysis
- Fault detection and classification
- Anomaly identification
## 📈 Key Features
- ✅ End-to-end ML/DL pipelines
- ✅ Comprehensive data preprocessing
- ✅ Advanced feature engineering
- ✅ Multiple model architectures
- ✅ Hyperparameter optimization
- ✅ Model interpretability (SHAP, LIME)
- ✅ Performance metrics and validation
- ✅ Visualization dashboards
## 🤝 Contributing
Contributions are welcome! Please read the contributing guidelines before submitting pull requests.
## 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 👤 Author
**Kartik**
- GitHub: [@kartik-commits](https://github.com/kartik-commits)
## 📚 References
- NASA Bearing Dataset
- CWRU Bearing Dataset
- UCI Machine Learning Repository
- Kaggle Manufacturing Datasets
- ASHRAE Building Energy Dataset
## 🔗 Useful Links
- [Issue Tracker](https://github.com/kartik-commits/mechanical-engineering-analytics/issues)
- [Discussions](https://github.com/kartik-commits/mechanical-engineering-analytics/discussions)
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Last Updated: 2025-10-11