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An AI-driven Prognostics and Health Management (PHM) system for predictive maintenance. This project features a FastAPI for real-time condition monitoring, advanced models for fault detection, and Remaining Useful Life (RUL) prediction. It integrates digital twin concepts and Explainable AI (XAI) for transparent and reliable industrial asset manage

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CodeDragon03/PLUSE

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Poster

PULSE (Predictive Uptime & Lifecycle Sentry Engine)

This AI-driven Prognostics and Health Management (PHM) platform utilizes a FastAPI backend for proactive, transparent, and intelligent asset management. It combines real-time condition monitoring with advanced models for fault detection and RUL prediction, integrating Digital Twin concepts with Explainable AI (XAI) to deliver clear, actionable insights for mission-critical predictive maintenance.

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Table of Contents

Getting Started

  1. Clone the repository:

    git clone https://github.com/CodeDragon03/PLUSE.git
  2. Install dependencies:

    cd PLUSE
    
    pnpm install

Usage

Use the following command to run the application:

python3 src/main.py

Contributing

We welcome contributions! To get started, please follow these steps:

  1. Fork the repository Click the "Fork" button at the top right of this page to create your own copy of the repository.

  2. Clone your fork

    git clone https://github.com/CodeDragon03/PLUSE.git
    
    cd PLUSE
  3. Create a new branch Use a descriptive branch name for your feature or bugfix.

    git checkout -b feature/your-feature-name
  4. Make your changes Implement your feature or fix the bug. Add or update tests and documentation as needed.

  5. Commit your changes

    git add --all
    
    git commit -m "Describe your changes"
  6. Push to your fork

    git push origin feature/your-feature-name
  7. Open a Pull Request Go to the original repository and click "New Pull Request". Select your branch and describe your changes.

Contribution Tips:

  • Follow the project's coding style and guidelines.
  • Write clear, concise commit messages.
  • Ensure all tests pass before submitting.
  • Be responsive to feedback on your pull request.

Contributors

Thanks to all the people who have contributed to this project!

Issues

If you encounter any issues, please open an issue in the Issues section.

Issue Guidelines

  • Search for existing issues before creating a new one.
  • Provide a clear and descriptive title.
  • Include steps to reproduce the issue, expected behavior, and actual behavior.
  • Attach relevant logs, screenshots, or code snippets if possible.
  • Be respectful and constructive in your communication.

Author

  Jay Yadav @CodeDragon03

Appendix

References

License

This project is licensed under the MIT License.

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An AI-driven Prognostics and Health Management (PHM) system for predictive maintenance. This project features a FastAPI for real-time condition monitoring, advanced models for fault detection, and Remaining Useful Life (RUL) prediction. It integrates digital twin concepts and Explainable AI (XAI) for transparent and reliable industrial asset manage

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