🏭 Interactive Industrial Data Dashboard

🌐 Language / Idioma: You are reading the English version. | Leer en Español → READMEES.md

This project is the visualization layer (Frontend) of the IIoT ecosystem. It is an interactive web platform built with Streamlit and Plotly that allows managers and operators to explore maintenance and operational data in real time.

---

🎯 Problem it Solves

Having data isolated in databases or spreadsheets prevents agile decision-making. This dashboard democratizes information through:

• Real-Time Visualization: Energy trend charts and cost distribution analysis.
• Dynamic Filters: Data exploration by date, area, equipment, and operational status.
• Engineering KPIs: On-the-fly calculation of MTBF (Mean Time Between Failures), Maintenance Costs, and Critical Downtime.
• Export: Ability to download the current filtered data view as CSV for external analysis.

🚀 What's New in Version 2.0 (5-Star Improvements)

• MVC Architecture: Full separation of data logic (data_engine.py) from the interface (app.py).
• Realistic Simulator: The data generator now includes real mathematical correlations (more usage hours = higher failure probability).
• Advanced Metrics: Integration of estimated MTBF calculation.
• Test Suite: Unit tests with pytest to ensure the accuracy of engine calculations.
• Data Export: New button to download the filtered data subset.
• Glassmorphism UI: Design optimized for Dark Mode with transparent Plotly charts.

🛠️ Technology Stack

Tool	Usage
Streamlit	Framework for Web App development
Pandas	DataEngine processing and filtering
Plotly	Interactive visualizations (Dark Theme)
Pytest	Data engine integrity validations

📁 Project Structure

dash_data/
├── tests/                      # Unit test suite
│   └── test_dashboard.py       # 6 data engine tests
├── app.py                      # User interface (Streamlit)
├── data_engine.py              # Backend logic (IndustrialDataManager)
├── generar_datos.py            # Realistic operation history simulator
├── data_industrial.csv         # Test dataset (2 years of operation)
├── requirements.txt            # Dependencies
├── PROYECTO_4_PLAN.md          # Planning document
└── README.md

⚙️ Installation & Usage

1. Install dependencies:

   pip install -r requirements.txt

2. Generate a new dataset (optional):

   # Generates a 2-year history with real correlations
   python generar_datos.py

3. Launch the Dashboard:

   streamlit run app.py

🧪 Unit Tests

pytest tests/ -v

---

Author: Gabriel Castro - Automation & IIoT Specialist.