AERISK (Aviation Risk Analysis System)

A predictive maintenance solution for the Airport Authority of India (AAI)

Overview

AERISK (Aviation Risk Analysis System) is an advanced tool designed to assist in risk analysis and fault prediction of aircraft components. By leveraging machine learning models, this system helps maintenance teams identify potential failures before they occur, ensuring aviation safety and operational efficiency.

Quick Navigation

Where	Why
Active Issues	Find all the current listed issues which requires completion
Roadmap & deadlines	To ensure smooth movements, check the deadlines/roadmap to completion of each issue

Deployment

Component	Platform	URL
Frontend	Vercel	https://risk-analysis-fault-prediction.vercel.app/
Backend	Render	https://aai-risk-analysis-fault-prediction.onrender.com/docs

Project Workflow

Step	Description
1	Upload aircraft component data in .csv format
2	Select prediction model from dropdown (if multiple models are available)
3	System processes data through the ML model
4	View comprehensive risk analysis/dashboard and fault predictions
5	Generate and export maintenance reports

System Flow

🚨 Click to expand the Mermaid flowchart

flowchart TD
    A[Start] --> B[Upload CSV Data]
    B --> C{Multiple components Available?}
    C -->|Yes| D[Select component from Dropdown]
    C -->|No| F
    D --> F[Process Data]
    F --> G[Run Prediction Algorithm]
    G --> H[Generate Risk Analysis]
    H --> I[Display Results Dashboard]
    I --> J{Export Report?}
    J -->|Yes| K[Download Report]
    J -->|No| L[End]
    K --> L

Loading

File structure

(as of 04-02-2026)

🚨 Click to view Project Structure

AAI_Risk analysis_Fault Prediction
├── .gitignore
├── CONTRIBUTING.md
├── LICENSE
├── ReadMe.md
├── .github
│   └── workflows
│       ├── keep_alive.yml
│       └── WORKFLOW_GUIDE.md
├── Client
│   ├── .env.local
│   ├── .env.production
│   ├── .gitignore
│   ├── eslint.config.js
│   ├── index.html
│   ├── package.json
│   ├── package-lock.json
│   ├── README.md
│   ├── vite.config.js
│   ├── public
│   │   └── logo.png
│   └── src
│       ├── App.jsx
│       ├── index.css
│       ├── main.jsx
│       ├── assets
│       │   └── react.svg
│       ├── components
│       │   ├── Footer.jsx
│       │   ├── Header.jsx
│       │   ├── Layout.jsx
│       │   └── Navbar.jsx
│       └── pages
│           ├── About.jsx
│           ├── Dashboard.jsx
│           ├── Home.jsx
│           └── Tools.jsx
├── Model
│   ├── durability.pkl
│   ├── LandingGearRUL.pkl
│   ├── dataset
│   │   ├── aerospace_structural_design_dataset.csv
│   │   ├── LandingGear_Balanced_Dataset.csv
│   │   └── CMAPSSData
│   │       ├── Damage Propagation Modeling.pdf
│   │       ├── readme.txt
│   │       ├── RUL_FD001.txt
│   │       ├── RUL_FD002.txt
│   │       ├── RUL_FD003.txt
│   │       ├── RUL_FD004.txt
│   │       ├── test_FD001.txt
│   │       ├── test_FD002.txt
│   │       ├── test_FD003.txt
│   │       ├── test_FD004.txt
│   │       ├── train_FD001.txt
│   │       ├── train_FD002.txt
│   │       ├── train_FD003.txt
│   │       ├── train_FD004.txt
│   │       └── .ipynb_checkpoints
│   │           └── train_FD001-checkpoint.txt
│   ├── Jupyter Notebook
│   │   ├── durabilityrequirements.txt
│   │   ├── LandingGear.ipynb
│   │   ├── remaining_useful_life.ipynb
│   │   └── structural_integrity.ipynb
│   └── rul_lstm_model
│       ├── fingerprint.pb
│       ├── keras_metadata.pb
│       ├── saved_model.pb
│       └── variables
│           ├── variables.data-00000-of-00001
│           └── variables.index
└── Server
    ├── .python-version
    ├── Dockerfile
    ├── fly.toml
    ├── README.md
    ├── requirements.txt
    ├── runtime.txt
    └── app
        ├── __init__.py
        ├── main.py
        └── model_registry.py

Recreate the latest directory structure by the following command in the Powershell(Windows) terminal:

# Install once
Install-Module PSTree -Scope CurrentUser

# Use whenever needed
Get-PSTree -Recurse -Exclude "node_modules", ".venv", "__pycache__" | Select-Object -ExpandProperty Hierarchy

Tech Stack

Machine Learning

• Python - Core ML development
• Pickle - Reading & writing of the model
• Scikit-learn - Model training and evaluation
• Pandas & NumPy - Data processing and analysis
• TensorFlow - Deep learning models

Backend

• FastAPI - API framework
• Joblib/Pickle - Model serialization

Frontend

• React - User interface
• Tailwind CSS - Styling
• Axios - API communication

Deployment

• Frontend - Vercel
• Backend - Render

Key Features

• will be added soon

Installation

• will be added soon

Usage

1. Launch the application
2. Navigate to the upload section
3. Select and upload your component data (.csv)
4. Choose the appropriate prediction model
5. Review risk analysis and predictions
6. Export reports as needed

Data Format Requirements

Input CSV files should include:

• will be added soon

Contributing

This is a private project with restricted access.

Before contributing:

1. Read CONTRIBUTING.md thoroughly
2. Understand the existing codebase structure
3. Ensure you have been assigned a specific task
4. Only work on your assigned responsibilities

Key points:

• Git is initialized in the root directory only
• Do not modify the existing file structure
• Only assigned team members may contribute
• Follow code guidelines and PR process outlined in CONTRIBUTING.md

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Ensuring aviation safety through predictive analytics