This is more than just a data science project — it’s a story of innovation, inspired by Rolls-Royce’s IntelligentEngine vision. A few months ago, I became fascinated with how Rolls-Royce builds intelligent engines that "talk back" — digital twins that predict and communicate when something will break, long before it actually does.
So I built one myself.
This platform simulates an intelligent aircraft engine environment:
- Predicts Remaining Useful Life (RUL) with machine learning
- Uses a LangChain-powered AI agent to make autonomous decisions
- Generates GPT-based natural language maintenance reports
- Offers real-time dashboards for engineers and managers
All deployed in a production-grade stack (FastAPI + Streamlit + Docker + Cloud).
| Feature | Description |
|---|---|
| RUL Prediction | Predicts Remaining Useful Life using NASA CMAPSS sensor data |
| FastAPI Backend | RESTful API for serving RUL predictions |
| Streamlit Dashboard | Real-time dashboard with sensor visualizations + decision outcomes |
| LangChain Agent | Makes autonomous decisions based on RUL and sensor health |
| GPT-4 Maintenance Reports | Generates human-readable insights from raw predictions |
| Dockerized & Cloud-ready | Easy to deploy on Render, GCP, AWS, or Docker Desktop |
Built with ❤️ to demonstrate intelligent aircraft monitoring systems. Inspired by Rolls-Royce's "IntelligentEngine" vision.
- Project by: Chandrika Joshi
- GitHub: