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🤖 CYBER ROBOTS AI - Predictive Maintenance System

Python Streamlit scikit-learn License

An advanced AI-powered predictive maintenance system for industrial equipment monitoring

Features • Installation • Usage • Models • Demo

📋 Table of Contents

🎯 Overview

CYBER ROBOTS AI is an intelligent predictive maintenance system that leverages state-of-the-art machine learning algorithms to predict equipment failures before they occur. The system provides real-time analytics, interactive 3D visualizations, and comprehensive health monitoring dashboards, enabling proactive maintenance decisions and reducing unexpected downtime.

Why Predictive Maintenance?

  • Reduce Costs: Prevent costly unexpected equipment failures
  • Increase Uptime: Maintain optimal operational efficiency
  • Data-Driven Decisions: Make informed maintenance scheduling decisions
  • Safety: Identify potential hazards before they become critical

✨ Key Features

🔮 Real-Time Predictive Analytics

  • Instant equipment health assessment using ML models
  • Remaining Useful Life (RUL) prediction in operational hours
  • Maintenance requirement classification (Normal/Needs Maintenance)
  • Anomaly detection through unsupervised learning

📊 Interactive Dashboard

  • 3D Sensor Visualization: Explore multi-dimensional sensor relationships
  • Health Gauge: Real-time system health scoring (0-100)
  • Distribution Charts: Visualize maintenance status and RUL distribution
  • Time Series Analysis: Track sensor trends over operational hours

📁 Batch Processing

  • Upload CSV files for bulk equipment analysis
  • Process multiple equipment records simultaneously
  • Export prediction results with detailed metrics
  • Support for custom sensor configurations

🎨 Advanced Visualizations

  • 3D Scatter Plots: Interactive sensor space exploration
  • Heatmaps: Correlation analysis between sensors
  • Box Plots: Statistical distribution of sensor values
  • Line Charts: Temporal trend analysis

🛠 Technology Stack

Category Technology Version Purpose
Backend Python 3.10+ Core programming language
Web Framework Streamlit 1.29.0 Interactive web application
Data Processing Pandas 2.1.3 Data manipulation
NumPy 1.26.2 Numerical computing
Visualization Matplotlib 3.8.2 Static plots
Seaborn 0.13.0 Statistical graphics
Plotly 5.18.0 Interactive 3D charts
Machine Learning scikit-learn 1.3.2 ML models & preprocessing
UI Components streamlit-option-menu 0.3.6 Enhanced navigation

🧠 Machine Learning Models

1. Random Forest Regressor (RUL Prediction)

Model: RandomForestRegressor(n_estimators=100, random_state=42)
Features: [sensor_1, sensor_2, sensor_3, operational_hours]
Target: RUL (Remaining Useful Life)
Output: Continuous value (hours)
Preprocessing: StandardScaler normalization

Purpose: Predicts the remaining operational hours before maintenance is required.

2. Random Forest Classifier (Maintenance Status)

Model: RandomForestClassifier(n_estimators=100, random_state=42)
Features: [sensor_1, sensor_2, sensor_3, operational_hours]
Target: maintenance (0=Normal, 1=Needs Maintenance)
Output: Binary classification

Purpose: Determines whether equipment requires immediate maintenance attention.

3. K-Means Clustering (Anomaly Detection)

Model: KMeans(n_clusters=2, random_state=42)
Features: [sensor_1, sensor_2, sensor_3, operational_hours]
Output: Cluster assignment (0=Normal, 1=Anomaly)

Purpose: Identifies unusual sensor patterns that may indicate emerging issues.

📥 Installation

Prerequisites

Before installation, ensure you have:

  • Python 3.10 or higher
  • pip (Python package manager)
  • Git
  • 4GB+ RAM recommended
  • Modern web browser (Chrome, Firefox, Safari, Edge)

Step-by-Step Installation

  1. Clone the Repository
git clone https://github.com/Mohammad-Khaliafah/Predictive-Maintenance-System.git
cd Predictive-Maintenance-System
  1. Create Virtual Environment (Recommended)
# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate
  1. Install Dependencies
pip install -r requirements.txt
  1. Verify Installation
python -c "import streamlit; print(streamlit.__version__)"

🚀 Usage

Starting the Application

  1. Navigate to Project Directory
cd Predictive-Maintenance-System
  1. Launch Streamlit App
streamlit run app.py
  1. Access the Application
  • The app will automatically open in your default browser
  • Default URL: http://localhost:8501
  • Network URL will be displayed in terminal for external access

Using the Dashboard

🏠 Home Page

  • View overall system statistics
  • Monitor real-time health metrics
  • Explore interactive 3D visualizations
  • Review system capabilities

📊 Historical Data

  • Browse the complete dataset
  • View data statistics
  • Download sample format for uploads
  • Understand data structure

📤 Input Data

Option 1: Upload CSV

  1. Navigate to "Input Data" → "Upload CSV" tab
  2. Click "Drop your CSV file here"
  3. Select file with required columns:
    • sensor_1
    • sensor_2
    • sensor_3
    • operational_hours
  4. Click "Process Data"
  5. View results and download predictions

Option 2: Generate Random Data

  1. Navigate to "Input Data" → "Generate Random" tab
  2. Click "Generate Random Values"
  3. Review generated sensor values
  4. Click "Use These Values"
  5. Check Results page for predictions

Option 3: Manual Input

  1. Navigate to "Input Data" → "Manual Input" tab
  2. Adjust sliders for each sensor value
  3. Set operational hours
  4. Click "Submit"
  5. View predictions on Results page

📈 Results

  • View prediction summaries
  • Analyze maintenance requirements
  • Review anomaly detection results
  • Download detailed reports

📉 Visualizations

  • Explore histograms of sensor readings
  • View scatter plots vs operational hours
  • Analyze RUL trends over time
  • Study correlation heatmaps
  • Review box plots for distribution analysis

📁 Project Structure

Predictive-Maintenance-System/
│
├── app.py                          # Main Streamlit application
├── requirements.txt                # Python dependencies
├── README.md                       # Project documentation
│
├── Predictive Maintenance.ipynb    # Jupyter notebook with model training
├── machinery_data.csv              # Sample dataset
│
└── .gitignore                      # Git ignore file

File Descriptions

  • app.py: Main application file containing the Streamlit interface and ML model integration
  • Predictive Maintenance.ipynb: Development notebook with data generation, model training, and evaluation
  • machinery_data.csv: Sample industrial equipment sensor data (1000 records)
  • requirements.txt: All Python package dependencies with versions

📊 Data Format

Required CSV Columns

Column Type Description Range
sensor_1 Float Primary sensor reading Normalized: -3 to 3
sensor_2 Float Secondary sensor reading Normalized: -3 to 3
sensor_3 Float Tertiary sensor reading Normalized: -3 to 3
operational_hours Integer Equipment running hours 100 - 5000

Example Data

sensor_1,sensor_2,sensor_3,operational_hours
0.496714,-0.138264,0.647689,2256
-0.234153,1.523030,-0.234137,3993
-0.469474,0.542560,-0.463418,1811
0.241962,-1.913280,-1.724918,4859

Optional Output Columns (After Prediction)

Column Type Description
Predicted_RUL Float Predicted remaining useful life (hours)
Maintenance_Status String "Normal" or "Needs Maintenance"
Anomaly_Status String "Normal" or "Anomaly"

🤝 Contributing

Contributions are welcome! Please follow these guidelines:

How to Contribute

  1. Fork the Repository
git fork https://github.com/Mohammad-Khaliafah/Predictive-Maintenance-System.git
  1. Create Feature Branch
git checkout -b feature/AmazingFeature
  1. Commit Changes
git commit -m 'Add some AmazingFeature'
  1. Push to Branch
git push origin feature/AmazingFeature
  1. Open Pull Request

Development Guidelines

  • Follow PEP 8 style guide for Python code
  • Add comments for complex logic
  • Update documentation for new features
  • Test thoroughly before submitting PR
  • Include screenshots for UI changes

Areas for Contribution

  • 🐛 Bug fixes
  • ✨ New features
  • 📝 Documentation improvements
  • 🎨 UI/UX enhancements
  • ⚡ Performance optimizations
  • 🧪 Additional test coverage

🔧 Troubleshooting

Common Issues

Issue: Port already in use

# Solution: Specify different port
streamlit run app.py --server.port 8502

Issue: Module not found error

# Solution: Reinstall requirements
pip install -r requirements.txt --force-reinstall

Issue: CSV upload fails

Solution: Ensure CSV has exact column names:
- sensor_1 (not Sensor_1 or sensor1)
- sensor_2
- sensor_3
- operational_hours

Issue: Predictions seem incorrect

Solution: Check that:
1. Sensor values are within reasonable ranges
2. Operational hours are positive integers
3. Data is not corrupted or contains NaN values

Performance Issues

If the app runs slowly:

  1. Reduce dataset size for testing
  2. Close other applications
  3. Check available RAM
  4. Use virtual environment

Getting Help

📜 License

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

MIT License

Copyright (c) 2025 Mohammad Khaliafah

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

🙏 Acknowledgments

  • Streamlit team for the amazing framework
  • scikit-learn contributors for ML tools
  • Plotly team for interactive visualizations
  • Open-source community

📞 Contact

Mohammad Khaliafah

⭐ Star this repository if you find it helpful!

Made with ❤️ by Mohammad Khaliafah

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