README.md

metadata

title: Jet Engine Predictive Maintenance
emoji: ✈️
colorFrom: blue
colorTo: red
sdk: streamlit
sdk_version: 1.31.0
python_version: '3.10'
app_file: app.py
pinned: false

✈️ Jet Engine Predictive Maintenance System

B.Tech AI & Data Science - Special Project

This project implements an Industrial AI solution to predict the Remaining Useful Life (RUL) of turbofan engines. By analyzing multivariate time-series data from 21 sensors, the system identifies degradation patterns and provides proactive maintenance alerts.

---

📌 Project Overview

Traditional maintenance is either Reactive (fixing after failure) or Preventive (fixing on a schedule). This project uses Predictive Maintenance, which uses machine learning to forecast exactly when a part will fail, saving costs and improving safety.

Key Features:

• Real-time RUL Prediction: Forecasts how many flight cycles remain before an engine requires service.
• Interactive Dashboard: Built with Streamlit, allowing users to input sensor readings manually.
• Health Analytics: Visualizes engine health status through a dynamic Gauge chart (Healthy, Caution, Critical).
• Machine Learning Backend: Uses an XGBoost Regressor optimized for time-series tabular data.

---

🛠️ Technical Stack

• Language: Python 3.10
• Model: XGBoost (Extreme Gradient Boosting)
• Web Framework: Streamlit
• Libraries: Pandas, NumPy, Scikit-learn, Joblib, Plotly
• Deployment: Hugging Face Spaces

---

📁 Repository Structure

File	Description
app.py	The main Streamlit web application.
engine_model.pkl	The trained AI model (XGBoost).
requirements.txt	Lists the Python packages needed to run the app.
train_local.py	(Optional) The script used to train the model locally.

---

⚙️ How to Use

1. Enter Sensor Readings: Adjust the sliders in the sidebar with data from your test set.
2. Analyze: Click the "Analyze Engine Health" button.
3. Review Results: The Gauge chart will indicate the remaining life. If the RUL is below 30 cycles, a Critical Warning will be triggered.

---

👨‍🎓 Author

[Your Name] 3rd Year B.Tech, AI & Data Science
[Your University Name]