Production Line Control and Analysis System

This project consists of two main components:

1. Production Line Control System: Real-time monitoring and control for manufacturing operations
2. Production Line Performance Analysis: Historical data analysis with anomaly detection

Project Overview

This system provides a comprehensive solution for manufacturing environments, combining:

• Real-time production monitoring and control
• Historical data analysis for quality improvement
• Anomaly detection to identify potential issues
• Performance visualization and reporting

Demo Video

Check out the demo video to see the system in action:

Components

1. Production Line Control System

A real-time dashboard for monitoring and controlling manufacturing operations:

• Live sensor data visualization
• Order management and tracking
• Production scheduling
• Anomaly alerts

Tech Stack:

• Frontend: React with TypeScript
• Backend: C# (.NET 6+) with SignalR for real-time updates
• Database: SQL Server

2. Production Line Performance Analysis

Data analysis tools focused on manufacturing performance and quality improvement:

• Historical data analysis
• Pattern recognition
• Anomaly detection
• Performance reporting

Tech Stack:

• Python 3.8+
• Flask for web interface
• scikit-learn for machine learning
• Pandas for data processing

System Architecture

The system follows a modular architecture with these key components:

├── Production Line Control (C# + React)
│   ├── Real-time Dashboard
│   ├── Order Management
│   └── Alerts System
└── Performance Analysis (Python)
    ├── Data Processing
    ├── Anomaly Detection
    └── Visualization

Getting Started

Setting up the Production Line Control System

1. Clone the repository

   git clone https://github.com/yourusername/production-line-control.git
   cd production-line-control
   

2. Backend setup

   cd backend/csharp/ProductionLineAPI
   dotnet restore
   dotnet run
   

3. Frontend setup

   cd frontend
   npm install
   npm start
   

4. Open your browser to http://localhost:1234

Setting up the Production Line Performance Analysis

1. Set up Python environment

   cd analysis
   python -m venv venv
   venv\Scripts\activate  # On Windows
   source venv/bin/activate  # On Unix/macOS
   pip install -r requirements.txt
   

2. Download and process the dataset

   The system uses the Bosch Production Line Performance dataset from Kaggle.

   python src/data/download.py
   python src/data/preprocess.py
   

   Note about Kaggle authentication:

   • If you have a Kaggle account, place your kaggle.json credentials file in ~/.kaggle/
   • If you encounter authentication issues, the system will automatically create synthetic data for development
   • The synthetic data mirrors the structure and statistical properties of the real dataset
   • To use real data, manually download from Kaggle and place in analysis/data/raw/

3. Run the anomaly detection model

   python src/models/anomaly.py
   

4. Start the web application

   python src/web/app.py
   

5. Open your browser to http://localhost:5050

Running the Complete System

For convenience, you can start all components using the provided startup script:

Windows:

.\startup.bat

macOS/Linux:

./startup.sh

Troubleshooting

If you encounter issues while running the system:

SignalR Connection Issues

We've improved connection handling for SignalR:

• Added ping/pong functionality to test connectivity
• Implemented robust connection retry with exponential backoff
• Improved CORS configuration for WebSocket connections

For detailed troubleshooting steps, see our troubleshooting guide.

Quick test:

1. Open your browser's developer console (F12)
2. Type window.pingSignalRServer()
3. You should see a "Received pong from server" message if connected

Common Issues

1. Port conflicts:

   • The C# API uses port 5000
   • The Python Analysis uses port 5050
   • The React frontend uses port 1234
   • If any port is already in use, modify the corresponding configuration

2. Kaggle API Authentication:

   • If you encounter "403 Forbidden" errors with the Kaggle API:
     • Verify your kaggle.json file has the correct permissions
     • Use the synthetic data generated automatically instead
     • The system will detect this scenario and create realistic test data

3. Python Environment:

   • Ensure all dependencies are installed with pip install -r requirements.txt
   • Python 3.8+ is required

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• This project was developed as a portfolio piece for showcasing control system software engineering skills
• Inspiration drawn from real-world industrial automation systems
• Thanks to the open-source community for the tools and libraries used in this project