PredictGuard: Intelligent Predictive Maintenance System

Project Overview

PredictGuard is an advanced AI-powered predictive maintenance system designed to revolutionize industrial equipment monitoring and maintenance.

Comprehensive Implementation Strategy

Technology Stack

• Backend: Python (FastAPI, asyncio)
• ML Models: PyTorch, TensorFlow
• Frontend: React.js
• IoT Integration: MQTT, Kafka
• Blockchain: Hyperledger Fabric
• AR Integration: Unity, WebXR

Key Repositories

1. Backend (Async Microservices Architecture)
2. Machine Learning Model Development
3. Frontend Visualization Dashboard
4. IoT Edge Processing
5. Blockchain Data Integrity Layer
6. AR Maintenance Assistant

Target Industries

• Automotive Manufacturing
• Aerospace Engineering
• Heavy Machinery Production
• Process Manufacturing

Sensor Selection Criteria

• Industrial-grade multi-modal sensors
• Recommended Brands:
  • Honeywell
  • Siemens
  • National Instruments
• Sensor Types:
  • Vibration
  • Temperature
  • Acoustic
  • Electrical

Compliance Considerations

• GDPR for Data Privacy
• ISO 27001 for Information Security
• Industrial Cybersecurity Standards (IEC 62443)

System Architecture

Core Components

• IoT Sensor Network
• Machine Learning Predictive Models
• Generative AI Scenario Simulation
• Natural Language Processing Log Analysis
• Comprehensive Visualization Dashboard
• Augmented Reality Maintenance Integration
• Blockchain Data Integrity Layer

Installation

Prerequisites

• Python 3.9+
• Docker
• Kubernetes (optional)

Setup Instructions

# Clone the repository
git clone https://github.com/CodeBong/PredictGuard.git

# Create virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the application
python main.py

Project Structure

PredictGuard/
│
├── src/
│   ├── core/
│   │   ├── __init__.py
│   │   ├── main.py
│   │   └── config.py
│   │
│   ├── sensors/
│   │   ├── __init__.py
│   │   ├── sensor_manager.py
│   │   └── data_collector.py
│   │
│   ├── ml_models/
│   │   ├── __init__.py
│   │   ├── anomaly_detector.py
│   │   ├── predictive_model.py
│   │   └── scenario_generator.py
│   │
│   ├── data_processing/
│   │   ├── __init__.py
│   │   ├── data_pipeline.py
│   │   └── data_validator.py
│   │
│   ├── blockchain/
│   │   ├── __init__.py
│   │   ├── data_integrity.py
│   │   └── record_manager.py
│   │
│   ├── nlp/
│   │   ├── __init__.py
│   │   └── log_analyzer.py
│   │
│   └── visualization/
│       ├── __init__.py
│       ├── dashboard.py
│       └── ar_interface.py
│
├── tests/
│   ├── test_anomaly_detector.py
│   ├── test_sensor_manager.py
│   └── test_data_pipeline.py
│
├── requirements.txt
├── README.md
└── config.yaml

Configuration

Modify config.yaml to customize:

• Sensor configurations
• Machine learning parameters
• Blockchain settings

API Endpoints

• /predict: Get maintenance predictions
• /sensors: Manage sensor configurations
• /dashboard: Access visualization interface

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License

Contact

• Project Lead: Advait Karnatak
• Co-Lead: Shreyash Kumar
• Email: predictguard@codebong.tech

Acknowledgments

• Industrial Maintenance Professionals
• AI and IoT Research Community